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ORCIDBriefings in Bioinformatics, Volume 23
Volume 23, Number 1, January 2022
- Umm-Kulthum Ismail Umlai
, Dhinoth Kumar Bangarusamy, Xavier Estivill, Puthen Veettil Jithesh:
Genome sequencing data analysis for rare disease gene discovery. - Wending Tang, Ruyu Dai, Wenhui Yan, Wei Zhang, Yannan Bin, En-Hua Xia, Junfeng Xia:
Identifying multi-functional bioactive peptide functions using multi-label deep learning. - Robson Bonidia, Douglas Silva Domingues, Danilo Sipoli Sanches, André C. P. L. F. de Carvalho:
MathFeature: feature extraction package for DNA, RNA and protein sequences based on mathematical descriptors. - Haochen Zhao, Shaokai Wang, Kai Zheng, Qichang Zhao, Feng Zhu, Jianxin Wang:
A similarity-based deep learning approach for determining the frequencies of drug side effects. - Peiran Jiang, Ying Chi, Xiao-Shuang Li, Xiang Liu, Xian-Sheng Hua, Kelin Xia:
Molecular persistent spectral image (Mol-PSI) representation for machine learning models in drug design. - Yaqi Wang, Guoqin Mai, Min Zou, Haoyu Long, Yao-Qing Chen, Litao Sun, Dechao Tian, Yang Zhao, Guozhi Jiang, Zicheng Cao, Xiangjun Du:
Heavy chain sequence-based classifier for the specificity of human antibodies. - Qingyong Wang, Yun Zhou:
FedSPL: federated self-paced learning for privacy-preserving disease diagnosis. - Eun-Gyeong Park, Sung-Jin Pyo, Youxi Cui, Sang-Ho Yoon, Jin-Wu Nam:
Tumor immune microenvironment lncRNAs. - Hui Li, Zhaohong Deng, Haitao Yang, Xiaoyong Pan, Zhisheng Wei, Hong-Bin Shen, Kup-Sze Choi, Lei Wang, Shitong Wang, Jing Wu:
circRNA-binding protein site prediction based on multi-view deep learning, subspace learning and multi-view classifier. - Kyle Hippe, Cade Lilley, Joshua William Berkenpas, Ciri Chandana Pocha, Kiyomi Kishaba, Hui Ding, Jie Hou, Dong Si, Renzhi Cao:
ZoomQA: residue-level protein model accuracy estimation with machine learning on sequential and 3D structural features. - Ran Su, Yingying Zhu, Quan Zou, Leyi Wei:
Distant metastasis identification based on optimized graph representation of gene interaction patterns. - Angela Serra, Michele Fratello, Antonio Federico, Ravi Ojha, Riccardo Provenzani, Ervin Tasnádi, Luca Cattelani, Giusy del Giudice, Pia Anneli Sofia Kinaret, Laura Aliisa Saarimäki, Alisa Pavel, Suvi Kuivanen, Vincenzo Cerullo, Olli Vapalahti, Peter Horváth, Antonio Di Lieto, Jari Yli-Kauhaluoma, Giuseppe Balistreri, Dario Greco:
Computationally prioritized drugs inhibit SARS-CoV-2 infection and syncytia formation. - Jinxian Wang, Ying Zhang, Wenjuan Nie, Yi Luo, Lei Deng:
Computational anti-COVID-19 drug design: progress and challenges. - Jhabindra Khanal, Hilal Tayara, Quan Zou, Kil To Chong:
DeepCap-Kcr: accurate identification and investigation of protein lysine crotonylation sites based on capsule network. - Yingxi Yang, Quan Sun, Le Huang, Jai G. Broome, Adolfo Correa, Alexander Reiner, Laura M. Raffield, Yuchen Yang, Yun Li:
eSCAN: scan regulatory regions for aggregate association testing using whole-genome sequencing data. - Fei Wang, Xiujuan Lei, Bo Liao, Fang-Xiang Wu:
Predicting drug-drug interactions by graph convolutional network with multi-kernel. - Yongqing Zhang, Zixuan Wang, Yuanqi Zeng, Yuhang Liu, Shuwen Xiong, Maocheng Wang, Jiliu Zhou, Quan Zou:
A novel convolution attention model for predicting transcription factor binding sites by combination of sequence and shape. - Qiguo Dai, Zhaowei Wang, Ziqiang Liu, Xiaodong Duan, Jinmiao Song, Maozu Guo:
Predicting miRNA-disease associations using an ensemble learning framework with resampling method. - Lianlian Wu, Yuqi Wen, Dongjin Leng, Qinglong Zhang, Chong Dai, Zhongming Wang, Ziqi Liu, Bowei Yan, Yixin Zhang, Jing Wang, Song He, Xiaochen Bo:
Machine learning methods, databases and tools for drug combination prediction. - Shuangquan Zhang, Anjun Ma, Jing Zhao, Dong Xu, Qin Ma, Yan Wang:
Assessing deep learning methods in cis-regulatory motif finding based on genomic sequencing data. - Wei Wang, Ruijiang Han, Menghan Zhang, Yuxian Wang, Tao Wang, Yongtian Wang, Xuequn Shang, Jiajie Peng:
A network-based method for brain disease gene prediction by integrating brain connectome and molecular network. - He Li, Hangxiao Zhang, Hangjin Jiang:
Combining power of different methods to detect associations in large data sets. - Ling Gao, Hui Cui, Tiangang Zhang, Nan Sheng, Ping Xuan:
Prediction of drug-disease associations by integrating common topologies of heterogeneous networks and specific topologies of subnets. - Guangzhan Zhang, Menglu Li, Huan Deng, Xinran Xu, Xuan Liu, Wen Zhang:
SGNNMD: signed graph neural network for predicting deregulation types of miRNA-disease associations. - Zhao Chen, Yin Jiang, Xiaoyu Zhang, Rui Zheng, Ruijin Qiu, Yang Sun, Chen Zhao, Hongcai Shang:
ResNet18DNN: prediction approach of drug-induced liver injury by deep neural network with ResNet18. - Lei Huang, Jiecong Lin, Xiangtao Li, Linqi Song, Zetian Zheng, Ka-Chun Wong:
EGFI: drug-drug interaction extraction and generation with fusion of enriched entity and sentence information. - Farzaneh Firoozbakht, Behnam Yousefi, Benno Schwikowski:
An overview of machine learning methods for monotherapy drug response prediction. - Xin An, Xi Chen, Daiyao Yi, Hongyang Li, Yuanfang Guan:
Representation of molecules for drug response prediction. - Ke Han, Long-Chen Shen, Yi-Heng Zhu, Jian Xu, Jiangning Song, Dong-Jun Yu:
MAResNet: predicting transcription factor binding sites by combining multi-scale bottom-up and top-down attention and residual network. - Xin Li, Xu Pan, Hanxiao Zhou, Peng Wang, Yue Gao, Shipeng Shang, Shuang Guo, Jie Sun, Zhiying Xiong, Shangwei Ning, Hui Zhi, Xia Li:
Comprehensive characterization genetic regulation and chromatin landscape of enhancer-associated long non-coding RNAs and their implication in human cancer. - Jianyuan Deng, Zhibo Yang, Iwao Ojima, Dimitris Samaras, Fusheng Wang:
Artificial intelligence in drug discovery: applications and techniques. - Xinyu Yu, Likun Jiang, Shuting Jin, Xiangxiang Zeng, Xiangrong Liu:
preMLI: a pre-trained method to uncover microRNA-lncRNA potential interactions. - Yingxin Kan, Limin Jiang, Yan Guo, Jijun Tang, Fei Guo:
Two-stage-vote ensemble framework based on integration of mutation data and gene interaction network for uncovering driver genes. - Adel Mehrpooya, Farid Saberi Movahed, Najmeh Azizi Zadeh, Mohammad Rezaei-Ravari, Farshad Saberi-Movahed, Mahdi Eftekhari, Iman Tavassoly:
High dimensionality reduction by matrix factorization for systems pharmacology. - Yi Yang, Xingjie Shi, Wei Liu, Qiuzhong Zhou, Mai Chan Lau, Jeffrey Chun Tatt Lim, Lei Sun, Cedric Chuan Young Ng, Joe Yeong, Jin Liu:
SC-MEB: spatial clustering with hidden Markov random field using empirical Bayes. - Robert Schwarz, Philipp Koch, Jeanne Wilbrandt, Steve Hoffmann:
Locus-specific expression analysis of transposable elements. - Qian Cheng, Shuqing Jiang, Feng Xu, Qian Wang, Yingjie Xiao, Ruyang Zhang, Jiuran Zhao, Jianbing Yan, Chuang Ma, Xiangfeng Wang:
Genome optimization via virtual simulation to accelerate maize hybrid breeding. - Nicoleta Siminea, Victor-Bogdan Popescu, José Ángel Sánchez Martín, Daniela Florea, Georgiana Gavril, Ana Maria Gheorghe, Corina Itcus, Krishna Kanhaiya, Octavian Pacioglu, Laura Ioana Popa, Romica Trandafir, Maria Iris Tusa, Manuela Sidoroff, Mihaela Paun, Eugen Czeizler, Andrei Paun, Ion Petre:
Network analytics for drug repurposing in COVID-19. - Leyun Wu, Cheng Peng, Yanqing Yang, Yulong Shi, Liping Zhou, Zhijian Xu, Weiliang Zhu:
Exploring the immune evasion of SARS-CoV-2 variant harboring E484K by molecular dynamics simulations. - Yang Guo, Fatemeh Esfahani, Xiaojian Shao, Venkatesh Srinivasan, Alex Thomo, Li Xing, Xuekui Zhang:
Integrative COVID-19 biological network inference with probabilistic core decomposition. - Chuanxing Li, Jing Gao, Zicheng Zhang, Lu Chen, Xun Li, Meng Zhou, Åsa M. Wheelock:
Multiomics integration-based molecular characterizations of COVID-19. - Hongfei Li, Yue Gong, Yifeng Liu, Hao Lin, Guohua Wang:
Detection of transcription factors binding to methylated DNA by deep recurrent neural network. - Lirong Zhang, Yanchao Yang, Lu Chai, Qianzhong Li, Junjie Liu, Hao Lin, Li Liu:
A deep learning model to identify gene expression level using cobinding transcription factor signals. - Fuyi Li, Shuangyu Dong, André Leier, Meiya Han, Xudong Guo, Jing Xu, Xiaoyu Wang, Shirui Pan, Cangzhi Jia, Yang Zhang, Geoffrey I. Webb, Lachlan J. M. Coin, Chen Li, Jiangning Song:
Positive-unlabeled learning in bioinformatics and computational biology: a brief review. - Shenggeng Lin, Yanjing Wang, Lingfeng Zhang, Yanyi Chu, Yatong Liu, Yitian Fang, Mingming Jiang, Qiankun Wang, Bowen Zhao, Yi Xiong, Dong-Qing Wei:
MDF-SA-DDI: predicting drug-drug interaction events based on multi-source drug fusion, multi-source feature fusion and transformer self-attention mechanism. - Yeji Wang, Shuo Wu, Yanwen Duan, Yong Huang:
A point cloud-based deep learning strategy for protein-ligand binding affinity prediction. - Jialu Hu, Yuanke Zhong, Xuequn Shang:
A versatile and scalable single-cell data integration algorithm based on domain-adversarial and variational approximation. - Menglu Li, Wen Zhang:
PHIAF: prediction of phage-host interactions with GAN-based data augmentation and sequence-based feature fusion. - Wei Zhang, Hanwen Xu, Rong Qiao, Bixi Zhong, Xianglin Zhang, Jin Gu, Xuegong Zhang, Lei Wei, Xiaowo Wang:
ARIC: accurate and robust inference of cell type proportions from bulk gene expression or DNA methylation data. - ZiaurRehman Tanoli, Jehad Aldahdooh, Farhan Alam, Yinyin Wang, Umair Seemab, Maddalena Fratelli, Petr Pavlis, Marián Hajdúch, Florence Bietrix, Philip Gribbon, Andrea Zaliani, Matthew D. Hall, Min Shen, Kyle R. Brimacombe, Evgeny Kulesskiy, Saarela Jani, Krister Wennerberg, Markus Vähä-Koskela, Jing Tang:
Minimal information for chemosensitivity assays (MICHA): a next-generation pipeline to enable the FAIRification of drug screening experiments. - Bingxiang Xu, Xiaoli Li, Xiaomeng Gao, Yan Jia, Jing Liu, Feifei Li, Zhihua Zhang:
DeNOPA: decoding nucleosome positions sensitively with sparse ATAC-seq data. - Lihua Jia, Wen Yao, Yingru Jiang, Yang Li, Zhizhan Wang, Haoran Li, Fangfang Huang, Jiaming Li, Tiantian Chen, Huiyong Zhang:
Development of interactive biological web applications with R/Shiny. - Hukam Chand Rawal, Shakir Ali, Tapan Kumar Mondal:
miRPreM and tiRPreM: Improved methodologies for the prediction of miRNAs and tRNA-induced small non-coding RNAs for model and non-model organisms. - Thanh-Binh Nguyen, Douglas E. V. Pires, David B. Ascher:
CSM-carbohydrate: protein-carbohydrate binding affinity prediction and docking scoring function. - Diego Forni, Rachele Cagliani, Chiara Pontremoli, Mario Clerici, Manuela Sironi:
The substitution spectra of coronavirus genomes. - Haoxiang Qin, Qidong Shen, Hongyi Zhao, Guozhen Qi, Lei Gao:
Network-based analysis revealed significant interactions between risk genes of severe COVID-19 and host genes interacted with SARS-CoV-2 proteins. - Hong Wang, Jingqing Zhang, Zhigang Lu, Weina Dai, Chuanjiang Ma, Yun Xiang, Yonghong Zhang:
Identification of potential therapeutic targets and mechanisms of COVID-19 through network analysis and screening of chemicals and herbal ingredients. - Ngoc Hieu Tran, Jinbo Xu, Ming Li:
A tale of solving two computational challenges in protein science: neoantigen prediction and protein structure prediction. - Wei Lan, Yi Dong, Qingfeng Chen, Ruiqing Zheng, Jin Liu, Yi Pan, Yi-Ping Phoebe Chen:
KGANCDA: predicting circRNA-disease associations based on knowledge graph attention network. - Sander N. Goossens, Tim H. Heupink, Elise De Vos, Anzaan Dippenaar, Margaretha De Vos, Rob Warren, Annelies Van Rie:
Detection of minor variants in Mycobacterium tuberculosis whole genome sequencing data. - Margaret G. Guo, Daniel N. Sosa, Russ B. Altman:
Challenges and opportunities in network-based solutions for biological questions. - Xiao-Rui Su, Lun Hu, Zhuhong You, Pengwei Hu, Lei Wang, Bo-Wei Zhao:
A deep learning method for repurposing antiviral drugs against new viruses via multi-view nonnegative matrix factorization and its application to SARS-CoV-2. - Neng Huang, Fan Nie, Peng Ni, Xin Gao, Feng Luo, Jianxin Wang:
BlockPolish: accurate polishing of long-read assembly via block divide-and-conquer. - Xu Pan, Caiyu Zhang, Junwei Wang, Peng Wang, Yue Gao, Shipeng Shang, Shuang Guo, Xin Li, Hui Zhi, Shangwei Ning:
Epigenome signature as an immunophenotype indicator prompts durable clinical immunotherapy benefits in lung adenocarcinoma. - Rufeng Li, Lixin Li, Yungang Xu, Juan Yang:
Erratum to: Machine learning meets omics applications and perspectives. - Deepak Nag Ayyala, Jianan Lin, Zhengqing Ouyang:
Differential RNA methylation using multivariate statistical methods. - Xinyun Guo, Huan He, Jialin Yu, Shaoping Shi:
PKSPS: a novel method for predicting kinase of specific phosphorylation sites based on maximum weighted bipartite matching algorithm and phosphorylation sequence enrichment analysis. - Cui-Xiang Lin, Hong-Dong Li, Chao Deng, Weisheng Liu, Shannon Erhardt, Fang-Xiang Wu, Xing-Ming Zhao, Yuanfang Guan, Jun Wang, Daifeng Wang, Bin Hu, Jianxin Wang:
An integrated brain-specific network identifies genes associated with neuropathologic and clinical traits of Alzheimer's disease. - Yuxin Song, Li'ang Yang, Li Jiang, Zhiyu Hao, Runqing Yang, Pao Xu:
Optimizing genomic control in mixed model associations with binary diseases. - Héctor Buena Maizón, Francisco J. Barrantes:
A deep learning-based approach to model anomalous diffusion of membrane proteins: the case of the nicotinic acetylcholine receptor. - Daudi Jjingo, Gerald Mboowa, Ivan Sserwadda, Robert Kakaire, Davis Kiberu, Marion Amujal, Ronald Galiwango, David Kateete, Moses Joloba, Christopher C. Whalen:
Bioinformatics mentorship in a resource limited setting. - Huan Liu, Quan Zou, Yun Xu:
A novel fast multiple nucleotide sequence alignment method based on FM-index. - Véronique Duboc, David Pratella, Marco Milanesio, John Boudjarane, Stéphane Descombes, Véronique Paquis-Flucklinger, Silvia Bottini:
NiPTUNE: an automated pipeline for noninvasive prenatal testing in an accurate, integrative and flexible framework. - Mingon Kang, Euiseong Ko, Tesfaye B. Mersha:
A roadmap for multi-omics data integration using deep learning. - Le Ou-Yang, Fan Lu, Zi-Chao Zhang, Min Wu:
Matrix factorization for biomedical link prediction and scRNA-seq data imputation: an empirical survey. - Fangfang Xia, Jonathan E. Allen, Prasanna Balaprakash, Thomas S. Brettin, Cristina Garcia-Cardona, Austin Clyde, Judith D. Cohn, James H. Doroshow, Xiaotian Duan, Veronika Dubinkina, Yvonne A. Evrard, Ya Ju Fan, Jason Gans, Stewart He, Pinyi Lu, Sergei Maslov, Alexander Partin, Maulik Shukla, Eric A. Stahlberg, Justin M. Wozniak, Hyun Seung Yoo, George F. Zaki, Yitan Zhu, Rick Stevens:
A cross-study analysis of drug response prediction in cancer cell lines. - Song Zhang, Kuerbannisha Amahong, Chenyang Zhang, Fengcheng Li, Jianqing Gao, Yunqing Qiu, Feng Zhu:
RNA-RNA interactions between SARS-CoV-2 and host benefit viral development and evolution during COVID-19 infection. - Liang Yu, Mingfei Xia, Qi An:
A network embedding framework based on integrating multiplex network for drug combination prediction. - Jinxian Wang, Xuejun Liu, Siyuan Shen, Lei Deng, Hui Liu:
DeepDDS: deep graph neural network with attention mechanism to predict synergistic drug combinations. - Bo-Wei Zhao, Lun Hu, Zhu-Hong You, Lei Wang, Xiao-Rui Su:
HINGRL: predicting drug-disease associations with graph representation learning on heterogeneous information networks. - Ye Hong, Dani Flinkman, Tomi Suomi, Sami Pietilä, Peter James, Eleanor Coffey, Laura L. Elo:
PhosPiR: an automated phosphoproteomic pipeline in R. - Meng-Huan Song, Chaochao Yan, Jiatang Li:
MEANGS: an efficient seed-free tool for de novo assembling animal mitochondrial genome using whole genome NGS data. - Qingyang Yin, Yang Wang, Jinting Guan, Guoli Ji:
scIAE: an integrative autoencoder-based ensemble classification framework for single-cell RNA-seq data. - Hao Wu, Yingfu Wu, Yuhong Jiang, Bing Zhou, Haoru Zhou, Zhongli Chen, Yi Xiong, Quanzhong Liu, Hongming Zhang:
scHiCStackL: a stacking ensemble learning-based method for single-cell Hi-C classification using cell embedding. - Zhen Cao, Yanting Huang, Ran Duan, Peng Jin, Zhaohui S. Qin, Shihua Zhang:
Disease category-specific annotation of variants using an ensemble learning framework. - Jeremiah Suryatenggara, Kol Jia Yong, Danielle E. Tenen, Daniel G. Tenen, Mahmoud A. Bassal:
ChIP-AP: an integrated analysis pipeline for unbiased ChIP-seq analysis. - Bo Zhang, Jianghua He, Jinxiang Hu, Devin C. Koestler, Prabhakar Chalise:
Letter to the Editor: on the stability and internal consistency of component-wise sparse mixture regression-based clustering. - Jiecong Lin, Lei Huang, Xingjian Chen, Shixiong Zhang, Ka-Chun Wong:
DeepMotifSyn: a deep learning approach to synthesize heterodimeric DNA motifs. - Fabrizio Kuruc, Harald Binder, Moritz Hess:
Stratified neural networks in a time-to-event setting. - Olufemi Aromolaran, Damilare Aromolaran, Itunuoluwa Isewon, Jelili Oyelade:
Corrigendum to: Machine learning approach to gene essentiality prediction: a review. - Xiwen Zhang, Weiwen Wang, Chuan-Xian Ren, Dao-Qing Dai:
Learning representation for multiple biological networks via a robust graph regularized integration approach. - Xu Zhang, Zhiqiang Ye, Jing Chen, Feng Qiao:
AMDBNorm: an approach based on distribution adjustment to eliminate batch effects of gene expression data. - Ashwin Dhakal, Cole McKay, John J. Tanner, Jianlin Cheng:
Artificial intelligence in the prediction of protein-ligand interactions: recent advances and future directions. - Chun-Chun Wang, Chi-Chi Zhu, Xing Chen:
Ensemble of kernel ridge regression-based small molecule-miRNA association prediction in human disease. - Dan Shao, Yinfei Dai, Nianfeng Li, Xuqing Cao, Wei Zhao, Li Cheng, Zhuqing Rong, Lan Huang, Yan Wang, Jing Zhao:
Artificial intelligence in clinical research of cancers. - Weining Yuan, Guanxing Chen, Calvin Yu-Chian Chen:
FusionDTA: attention-based feature polymerizer and knowledge distillation for drug-target binding affinity prediction. - María Virginia Sabando, Ignacio Ponzoni, Evangelos E. Milios, Axel J. Soto:
Using molecular embeddings in QSAR modeling: does it make a difference? - Francesco Napolitano, Xiaopeng Xu, Xin Gao:
Impact of computational approaches in the fight against COVID-19: an AI guided review of 17 000 studies. - Lis Arend, Judith Bernett, Quirin Manz, Melissa Klug, Olga Lazareva, Jan Baumbach, Dario Bongiovanni, Markus List:
A systematic comparison of novel and existing differential analysis methods for CyTOF data. - Priyank Shukla, Preeti Pandey, Bodhayan Prasad, Tony Robinson, Rituraj Purohit, Leon G. D'cruz, Murtaza M. Tambuwala, Ankur Mutreja, Jim Harkin, Taranjit Singh Rai, Elaine K. Murray, David S. Gibson, Anthony J. Bjourson:
Immuno-informatics analysis predicts B and T cell consensus epitopes for designing peptide vaccine against SARS-CoV-2 with 99.82% global population coverage. - Arnold K. Nyamabo, Hui Yu, Zun Liu, Jian-Yu Shi:
Drug-drug interaction prediction with learnable size-adaptive molecular substructures. - Sandra L. Taylor, Matthew Ponzini, Machelle D. Wilson, Kyoungmi Kim:
Comparison of imputation and imputation-free methods for statistical analysis of mass spectrometry data with missing data. - Maryam Mahjoubin-Tehran, Samaneh Rezaei, Amin Jalili, Amirhossein Sahebkar, Seyed Hamid Aghaee-Bakhtiari:
A comprehensive review of online resources for microRNA-diseases associations: the state of the art. - Yurui Chen, Louxin Zhang:
How much can deep learning improve prediction of the responses to drugs in cancer cell lines? - Yu-Jian Kang, Jing-Yi Li, Lan Ke, Shuai Jiang, Dechang Yang, Mei Hou, Ge Gao:
Quantitative model suggests both intrinsic and contextual features contribute to the transcript coding ability determination in cells. - Wenjia He, Yi Jiang, Junru Jin, Zhongshen Li, Jiaojiao Zhao, Balachandran Manavalan, Ran Su, Xin Gao, Leyi Wei:
Accelerating bioactive peptide discovery via mutual information-based meta-learning. - Ruohan Wang, Xiang-Li-Lan Zhang, Jianping Wang, Shuai Cheng Li:
DeepHost: phage host prediction with convolutional neural network. - Xiaosa Zhao, Xiaowei Zhao, Minghao Yin:
Heterogeneous graph attention network based on meta-paths for lncRNA-disease association prediction. - Mario Flores, Zhentao Liu, Tinghe Zhang, Md Musaddaqui Hasib, Yu-Chiao Chiu, Zhenqing Ye, Karla Paniagua, Sumin Jo, Jianqiu Zhang, Shou-Jiang Gao, Yu-Fang Jin, Yidong Chen, Yufei Huang:
Deep learning tackles single-cell analysis - a survey of deep learning for scRNA-seq analysis. - Fuhao Zhang, Bi Zhao, Wenbo Shi, Min Li, Lukasz A. Kurgan:
DeepDISOBind: accurate prediction of RNA-, DNA- and protein-binding intrinsically disordered residues with deep multi-task learning. - Hang Hu, Zhong Li, Xiangjie Li, Minzhe Yu, Xiutao Pan:
ScCAEs: deep clustering of single-cell RNA-seq via convolutional autoencoder embedding and soft K-means. - Junkang Wei, Siyuan Chen, Licheng Zong, Xin Gao, Yu Li:
Protein-RNA interaction prediction with deep learning: structure matters. - Jen-Hao Chen, Yufeng Jane Tseng:
A general optimization protocol for molecular property prediction using a deep learning network. - Chuanze Kang, Han Zhang, Zhuo Liu, Shenwei Huang, Yanbin Yin:
LR-GNN: a graph neural network based on link representation for predicting molecular associations. - Rufeng Li, Lixin Li, Yungang Xu, Juan Yang:
Machine learning meets omics: applications and perspectives. - Ping Xuan, Mengsi Fan, Hui Cui, Tiangang Zhang, Toshiya Nakaguchi:
GVDTI: graph convolutional and variational autoencoders with attribute-level attention for drug-protein interaction prediction. - Peng Li, Haoran Zhang, Wuxia Zhang, Yuanyuan Zhang, Lingmin Zhan, Ning Wang, Caiping Chen, Bangze Fu, Jinzhong Zhao, Xuezhong Zhou, Shuzhen Guo, Jianxin Chen:
TMNP: a transcriptome-based multi-scale network pharmacology platform for herbal medicine. - Tao Wang, Yongzhuang Liu, Quanwei Yin, Jiaquan Geng, Jin Chen, Xipeng Yin, Yongtian Wang, Xuequn Shang, Chunwei Tian, Yadong Wang, Jiajie Peng:
Enhancing discoveries of molecular QTL studies with small sample size using summary statistic imputation. - Rong Tang, Zijian Wu, Zeyin Rong, Jin Xu, Wei Wang, Bo Zhang, Xianjun Yu, Si Shi:
Ferroptosis-related lncRNA pairs to predict the clinical outcome and molecular characteristics of pancreatic ductal adenocarcinoma. - Jiacheng Wang, Quan Zou, Chen Lin:
A comparison of deep learning-based pre-processing and clustering approaches for single-cell RNA sequencing data. - Enrico Gaffo, Alessia Buratin, Anna Dal Molin, Stefania Bortoluzzi:
Sensitive, reliable and robust circRNA detection from RNA-seq with CirComPara2. - Chunxiang Wang, Zengchao Mu, Chaozhou Mou, Hongyu Zheng, Juntao Liu:
Consensus-based clustering of single cells by reconstructing cell-to-cell dissimilarity. - Ashmita Dey, Sagnik Sen, Ujjwal Maulik:
Study of transcription factor druggabilty for prostate cancer using structure information, gene regulatory networks and protein moonlighting. - Yu Sun, Haicheng Li, Lei Zheng, Jinzhao Li, Yan Hong, Pengfei Liang, Lai-Yu Kwok, Yongchun Zuo, Wenyi Zhang, Heping Zhang:
iProbiotics: a machine learning platform for rapid identification of probiotic properties from whole-genome primary sequences. - Xiangtao Li, Shaochuan Li, Lei Huang, Shixiong Zhang, Ka-Chun Wong:
High-throughput single-cell RNA-seq data imputation and characterization with surrogate-assisted automated deep learning. - Wenyang Zhou, Chang Xu, Pingping Wang, Anastasia A. Anashkina, Qinghua Jiang:
Impact of mutations in SARS-COV-2 spike on viral infectivity and antigenicity. - Gang Xu, Qinghua Wang, Jianpeng Ma:
OPUS-Rota4: a gradient-based protein side-chain modeling framework assisted by deep learning-based predictors. - Quang-Thai Ho, Nguyen-Quoc-Khanh Le, Yu-Yen Ou:
mCNN-ETC: identifying electron transporters and their functional families by using multiple windows scanning techniques in convolutional neural networks with evolutionary information of protein sequences. - Kamil Kaminski, Jan Ludwiczak, Maciej Jasinski, Adriana Bukala, Rafal Madaj, Krzysztof Szczepaniak, Stanislaw Dunin-Horkawicz:
Rossmann-toolbox: a deep learning-based protocol for the prediction and design of cofactor specificity in Rossmann fold proteins. - Hui-Sheng Li, Le Ou-Yang, Yuan Zhu, Hong Yan, Xiao-Fei Zhang:
scDEA: differential expression analysis in single-cell RNA-sequencing data via ensemble learning. - Shanchen Pang, Ying Zhang, Tao Song, Xudong Zhang, Xun Wang, Alfonso Rodríguez-Patón:
AMDE: a novel attention-mechanism-based multidimensional feature encoder for drug-drug interaction prediction. - Vishakha Singh, Sameer Shrivastava, Sanjay Kumar Singh, Abhinav Kumar, Sonal Saxena:
StaBle-ABPpred: a stacked ensemble predictor based on biLSTM and attention mechanism for accelerated discovery of antibacterial peptides. - Ritesh Sharma, Sameer Shrivastava, Sanjay Kumar Singh, Abhinav Kumar, Sonal Saxena, Raj Kumar Singh:
Deep-AFPpred: identifying novel antifungal peptides using pretrained embeddings from seq2vec with 1DCNN-BiLSTM. - Giulia Russo, Valentina Di Salvatore, Giuseppe Sgroi, Giuseppe Alessandro Parasiliti Palumbo, Pedro A. Reche, Francesco Pappalardo:
A multi-step and multi-scale bioinformatic protocol to investigate potential SARS-CoV-2 vaccine targets. - Yuan Luo:
Evaluating the state of the art in missing data imputation for clinical data. - Hao Lv, Yang Zhang, Jia-Shu Wang, Shi-Shi Yuan, Zi-Jie Sun, Fu-Ying Dao, Zheng-Xing Guan, Hao Lin, Ke-Jun Deng:
iRice-MS: An integrated XGBoost model for detecting multitype post-translational modification sites in rice. - Alejandro F. Villaverde, Dilan Pathirana, Fabian Fröhlich, Jan Hasenauer, Julio R. Banga:
A protocol for dynamic model calibration. - Qiu Xiao, Jianhua Dai, Jiawei Luo:
A survey of circular RNAs in complex diseases: databases, tools and computational methods. - Tiantian Liu, Peirong Xu, Yueyao Du, Hui Lu, Hongyu Zhao, Tao Wang:
MZINBVA: variational approximation for multilevel zero-inflated negative-binomial models for association analysis in microbiome surveys. - Andrea Bommert, Thomas Welchowski, Matthias Schmid, Jörg Rahnenführer:
Benchmark of filter methods for feature selection in high-dimensional gene expression survival data. - Xing-Xing Shi, Zhi-Zheng Wang, Yu-Liang Wang, Guang-Yi Huang, Jing-Fang Yang, Fan Wang, Ge-Fei Hao, Guangfu Yang:
PTMdyna: exploring the influence of post-translation modifications on protein conformational dynamics. - Bruna Moreira da Silva, Yoochan Myung, David B. Ascher, Douglas E. V. Pires:
epitope3D: a machine learning method for conformational B-cell epitope prediction. - Qiaoming Liu, Jun Wan, Guohua Wang:
A survey on computational methods in discovering protein inhibitors of SARS-CoV-2. - Siqi Bao, Ke Li, Congcong Yan, Zicheng Zhang, Jia Qu, Meng Zhou:
Deep learning-based advances and applications for single-cell RNA-sequencing data analysis. - Min Zeng, Yifan Wu, Chengqian Lu, Fuhao Zhang, Fang-Xiang Wu, Min Li:
DeepLncLoc: a deep learning framework for long non-coding RNA subcellular localization prediction based on subsequence embedding. - Wenjing Song, Weiwen Wang, Dao-Qing Dai:
Subtype-WESLR: identifying cancer subtype with weighted ensemble sparse latent representation of multi-view data. - Ping Xuan, Dong Wang, Hui Cui, Tiangang Zhang, Toshiya Nakaguchi:
Integration of pairwise neighbor topologies and miRNA family and cluster attributes for miRNA-disease association prediction. - Wei Peng, Qi Tang, Wei Dai, Tielin Chen:
Improving cancer driver gene identification using multi-task learning on graph convolutional network. - Yumeng Zhang, Yangming Zhang, Yi Xiong, Hui Wang, Zixin Deng, Jiangning Song, Hong-Yu Ou:
T4SEfinder: a bioinformatics tool for genome-scale prediction of bacterial type IV secreted effectors using pre-trained protein language model. - Fang Ge, Ying Zhang, Jian Xu, Muhammad Arif, Jiangning Song, Dong-Jun Yu:
Prediction of disease-associated nsSNPs by integrating multi-scale ResNet models with deep feature fusion. - R. Prabakaran, Puneet Rawat, Sandeep Kumar, M. Michael Gromiha:
Erratum to: Evaluation of in silico tools for the prediction of protein and peptide aggregation on diverse datasets. - Ting Wang, Haojie Lu, Ping Zeng:
Identifying pleiotropic genes for complex phenotypes with summary statistics from a perspective of composite null hypothesis testing. - Xiaoqing Peng, Hongze Luo, Xiangyan Kong, Jianxin Wang:
Metrics for evaluating differentially methylated region sets predicted from BS-seq data. - Maria K. Jaakkola, Laura L. Elo:
Estimating cell type-specific differential expression using deconvolution. - Chiara Gabella, Severine Duvaud, Christine Durinx:
Managing the life cycle of a portfolio of open data resources at the SIB Swiss Institute of Bioinformatics. - Tzu-Hui Yu, Bo-Han Su, Leo Chander Battalora, Sin Liu, Yufeng Jane Tseng:
Ensemble modeling with machine learning and deep learning to provide interpretable generalized rules for classifying CNS drugs with high prediction power. - Chunyan Ao, Quan Zou, Liang Yu:
NmRF: identification of multispecies RNA 2'-O-methylation modification sites from RNA sequences. - Minghua Hou, Chunxiang Peng, Xiaogen Zhou, Biao Zhang, Guijun Zhang:
Multi contact-based folding method for de novo protein structure prediction. - Kevin Bu, David S. Wallach, Zach Wilson, Nan Shen, Leopoldo N. Segal, Emilia Bagiella, José Carlos Clemente:
Identifying correlations driven by influential observations in large datasets. - Mingyang Wang, Huiyong Sun, Jike Wang, Jinping Pang, Xin Chai, Lei Xu, Honglin Li, Dongsheng Cao, Tingjun Hou:
Comprehensive assessment of deep generative architectures for de novo drug design. - Mengqi Zhang, Sahar Gelfman, Cristiane Araujo Martins Moreno, Janice M. McCarthy, Matthew B. Harms, David B. Goldstein, Andrew S. Allen:
Focused goodness of fit tests for gene set analyses. - Mengting Niu, Ying Ju, Chen Lin, Quan Zou:
Characterizing viral circRNAs and their application in identifying circRNAs in viruses. - Josh J. R. Burns, Benjamin T. Shealy, Mitchell S. Greer, John A. Hadish, Matthew McGowan, Tyler Biggs, Melissa C. Smith, F. Alex Feltus, Stephen P. Ficklin:
Addressing noise in co-expression network construction. - Jiankang Wang, Ryuichiro Nakato:
HiC1Dmetrics: framework to extract various one-dimensional features from chromosome structure data. - Boqiao Lai, Jinbo Xu:
Accurate protein function prediction via graph attention networks with predicted structure information. - Yu-Da Lin, Yi-Chen Lee, Chih-Po Chiang, Sin-Hua Moi, Jung-Yu Kan:
MOAI: a multi-outcome interaction identification approach reveals an interaction between vaspin and carcinoembryonic antigen on colorectal cancer prognosis. - Shu-Hao Wang, Chun-Chun Wang, Li Huang, Lian-Ying Miao, Xing Chen:
Dual-Network Collaborative Matrix Factorization for predicting small molecule-miRNA associations. - Kengo Sato, Yuki Kato:
Prediction of RNA secondary structure including pseudoknots for long sequences. - Jingxin Dong, Mingyi Zhao, Yuansheng Liu, Yansen Su, Xiangxiang Zeng:
Deep learning in retrosynthesis planning: datasets, models and tools. - Ji Dong, Peijie Zhou, Yichong Wu, Yidong Chen, Haoling Xie, Yuan Gao, Jiansen Lu, Jingwei Yang, Xiannian Zhang, Lu Wen, Tiejun Li, Fuchou Tang:
Integrating single-cell datasets with ambiguous batch information by incorporating molecular network features. - Bin Yang, Wenzheng Bao, Jinglong Wang:
Active disease-related compound identification based on capsule network. - Xiaofei Zhao, Allison C. Hu, Sizhen Wang, Xiaoyue Wang:
Calling small variants using universality with Bayes-factor-adjusted odds ratios. - Ali Mahdipour-Shirayeh, Natalie Erdmann, Chungyee Leung-Hagesteijn, Rodger E. Tiedemann:
sciCNV: high-throughput paired profiling of transcriptomes and DNA copy number variations at single-cell resolution. - Yue Liu, Junfeng Zhang, Shu-Lin Wang, Xiangxiang Zeng, Wei Zhang:
Are dropout imputation methods for scRNA-seq effective for scATAC-seq data? - Qiang Kang, Jun Meng, Chenglin Su, Yushi Luan:
Mining plant endogenous target mimics from miRNA-lncRNA interactions based on dual-path parallel ensemble pruning method. - Chen Wei, Mingkai Chen, Wenying Deng, Liangyu Bie, Yijie Ma, Chi Zhang, Kangdong Liu, Wei Shen, Shuyi Wang, Chaogang Yang, Suxia Luo, Ning Li:
Characterization of gastric cancer stem-like molecular features, immune and pharmacogenomic landscapes. - Yue Wang, Yunpeng Zhao, Qing Pan:
Advances, challenges and opportunities of phylogenetic and social network analysis using COVID-19 data. - Waqar Hussain:
sAMP-PFPDeep: Improving accuracy of short antimicrobial peptides prediction using three different sequence encodings and deep neural networks. - Zijun Zhu, Sainan Zhang, Ping Wang, Xinyu Chen, Jianxing Bi, Liang Cheng, Xue Zhang:
A comprehensive review of the analysis and integration of omics data for SARS-CoV-2 and COVID-19. - Yifan Deng, Yang Qiu, Xinran Xu, Shichao Liu, Zhongfei Zhang, Shanfeng Zhu, Wen Zhang:
META-DDIE: predicting drug-drug interaction events with few-shot learning. - Xuan Liu, Congzhi Song, Feng Huang, Haitao Fu, Wenjie Xiao, Wen Zhang:
GraphCDR: a graph neural network method with contrastive learning for cancer drug response prediction. - Lingxi Chen, Yuhao Qing, Ruikang Li, Chaohui Li, Hechen Li, Xikang Feng, Shuai Cheng Li:
Somatic variant analysis suite: copy number variation clonal visualization online platform for large-scale single-cell genomics. - Balachandran Manavalan, Shaherin Basith, Gwang Lee:
Comparative analysis of machine learning-based approaches for identifying therapeutic peptides targeting SARS-CoV-2. - Piotr Grzesik, Dariusz Rafal Augustyn, Lukasz Wycislik, Dariusz Mrozek:
Serverless computing in omics data analysis and integration. - Mohammad Reza Karimi, Amir-Hossein Karimi, Shamsozoha Abolmaali, Mehdi Sadeghi, Ulf Schmitz:
Prospects and challenges of cancer systems medicine: from genes to disease networks. - Yongxian Fan, Meijun Chen, Xiaoyong Pan:
GCRFLDA: scoring lncRNA-disease associations using graph convolution matrix completion with conditional random field. - Xuxu Wei, Xiang Wu, Zeyu Cheng, Qingming Wu, Chen Cao, Xue Xu, Hongcai Shang:
Botanical drugs: a new strategy for structure-based target prediction. - Qinhuan Luo, Yongzhen Yu, Xun Lan:
SIGNET: single-cell RNA-seq-based gene regulatory network prediction using multiple-layer perceptron bagging. - Zheng Jiang, Si-Rui Xiao, Rong Liu:
Dissecting and predicting different types of binding sites in nucleic acids based on structural information. - Xi Yang, Wei Wang, Jing-Lun Ma, Yan-Long Qiu, Kai Lu, Dong-Sheng Cao, Chengkun Wu:
BioNet: a large-scale and heterogeneous biological network model for interaction prediction with graph convolution. - Tengbo Zhang, Yaxu Li, Yanrong Yang, Linjun Weng, Zhiqiang Wu, Jiali Zhu, Jieling Qin, Qi Liu, Ping Wang:
iCRISEE: an integrative analysis of CRISPR screen by reducing false positive hits. - Mohammad Vahed, Majid Vahed, Lana X. Garmire:
BML: a versatile web server for bipartite motif discovery. - Hai-Cheng Yi, Zhu-Hong You, De-Shuang Huang, Chee Keong Kwoh:
Graph representation learning in bioinformatics: trends, methods and applications. - Yang Li, Guanyu Qiao, Keqi Wang, Guohua Wang:
Drug-target interaction predication via multi-channel graph neural networks. - Shaherin Basith, Gwang Lee, Balachandran Manavalan:
STALLION: a stacking-based ensemble learning framework for prokaryotic lysine acetylation site prediction.
Volume 23, Number 2, March 2022
- Anna Dal Molin, Enrico Gaffo, Valeria Difilippo, Alessia Buratin, Caterina Tretti Parenzan, Silvia Bresolin, Stefania Bortoluzzi:
CRAFT: a bioinformatics software for custom prediction of circular RNA functions. - Yuansong Zeng, Zhuoyi Wei, Zixiang Pan, Yutong Lu, Yuedong Yang:
A robust and scalable graph neural network for accurate single-cell classification. - Shih-Kai Chu, Shilin Zhao, Yu Shyr, Qi Liu:
Comprehensive evaluation of noise reduction methods for single-cell RNA sequencing data. - Guodong Li, Ping Zhang, Weicheng Sun, Chengjuan Ren, Lei Wang:
Bridging-BPs: a novel approach to predict potential drug-target interactions based on a bridging heterogeneous graph and BPs2vec. - Yanqiang Han, Zhilong Wang, An Chen, Imran Ali, Junfei Cai, Simin Ye, Jin-Jin Li:
An inductive transfer learning force field (ITLFF) protocol builds protein force fields in seconds. - Jiashuai Zhang, Huiting Xiao, Kai Song, Keru Li, Hengrui Yuan, Rongqiang Yuan, Jia Yang, Yuting Zhao, Zhiqiang Chang, Wenyuan Zhao:
IndGOterm: a qualitative method for the identification of individually dysregulated GO terms in cancer. - Hongda Zhang, Hui Cui, Tiangang Zhang, Yangkun Cao, Ping Xuan:
Learning multi-scale heterogenous network topologies and various pairwise attributes for drug-disease association prediction. - Biao Zhang, Dong Liu, Yang Zhang, Hong-Bin Shen, Gui-Jun Zhang:
Accurate flexible refinement for atomic-level protein structure using cryo-EM density maps and deep learning. - Ramachandran Chelliah, Eric Banan-Mwinedaliri, Imran Khan, Shuai Wei, Fazle Elahi, Su-Jung Yeon, Vijayalakshmi Selvakumar, Fred Kwame Ofosu, Momna Rubab, Hum Hun Ju, Harikrishna Reddy Rallabandi, Inamul Hasan Madar, Ghazala Sultan, Deog Hwan Oh:
A review on the application of bioinformatics tools in food microbiome studies. - Xiao Yuan, Jing Wang, Bing Dai, Yanfang Sun, Keke Zhang, Fangfang Chen, Qian Peng, Yixuan Huang, Xinlei Zhang, Junru Chen, Xilin Xu, Jun Chuan, Wenbo Mu, Huiyuan Li, Ping Fang, Qiang Gong, Peng Zhang:
Evaluation of phenotype-driven gene prioritization methods for Mendelian diseases. - Minerva Fatimae Ventolero, Saidi Wang, Haiyan Hu, Xiaoman Li:
Computational analyses of bacterial strains from shotgun reads. - Zhuohang Yu, Zengrui Wu, Weihua Li, Guixia Liu, Yun Tang:
ADENet: a novel network-based inference method for prediction of drug adverse events. - Zutan Li, Jingya Fang, Shining Wang, Liangyun Zhang, Yuanyuan Chen, Cong Pian:
Adapt-Kcr: a novel deep learning framework for accurate prediction of lysine crotonylation sites based on learning embedding features and attention architecture. - Haizhen Zheng:
Letter regarding article named 'Is acupuncture effective in the treatment of COVID-19 related symptoms? Based on bioinformatics/network topology strategy'. - Weiping Lin, Lianlian Wu, Yixin Zhang, Yuqi Wen, Bowei Yan, Chong Dai, Kun-Hong Liu, Song He, Xiaochen Bo:
An enhanced cascade-based deep forest model for drug combination prediction. - Liang Wang, Xavier Didelot, Yuhai Bi, George F. Gao:
SARS-CoV-2 transmissibility compared between variants of concern and vaccination status. - Hao Wu, Pengyu Zhang, Zhaoheng Ai, Leyi Wei, Hongming Zhang, Fan Yang, Lizhen Cui:
StackTADB: a stacking-based ensemble learning model for predicting the boundaries of topologically associating domains (TADs) accurately in fruit flies. - Alejandro A. Edera, Diego H. Milone, Georgina Stegmayer:
Anc2vec: embedding gene ontology terms by preserving ancestors relationships. - Yin Shen, Quan Zhong, Tian Liu, Zi Wen, Wei Shen, Li Li:
CharID: a two-step model for universal prediction of interactions between chromatin accessible regions. - Douglas E. V. Pires, Keith A. Stubbs, Joshua S. Mylne, David B. Ascher:
cropCSM: designing safe and potent herbicides with graph-based signatures. - Meng Zhang, Cangzhi Jia, Fuyi Li, Chen Li, Yan Zhu, Tatsuya Akutsu, Geoffrey I. Webb, Quan Zou, Lachlan J. M. Coin, Jiangning Song:
Critical assessment of computational tools for prokaryotic and eukaryotic promoter prediction. - Mahwish Shahid, Maham Ilyas, Waqar Hussain, Yaser Daanial Khan:
ORI-Deep: improving the accuracy for predicting origin of replication sites by using a blend of features and long short-term memory network. - Muhalb M. Alsaffar, Mohammad Hasan, Gavin P. McStay, Mohamed Sedky:
Digital DNA lifecycle security and privacy: an overview. - Mingzhe Xie, Ludong Yang, Gennong Chen, Yan Wang, Zhi Xie, Hongwei Wang:
RiboChat: a chat-style web interface for analysis and annotation of ribosome profiling data. - Ken Chen, Huiying Zhao, Yuedong Yang:
Capturing large genomic contexts for accurately predicting enhancer-promoter interactions. - Chenwei Wang, Xiaodan Tan, Dachao Tang, Yujie Gou, Cheng Han, Wanshan Ning, Shaofeng Lin, Weizhi Zhang, Miaomiao Chen, Di Peng, Yu Xue:
GPS-Uber: a hybrid-learning framework for prediction of general and E3-specific lysine ubiquitination sites. - Venket Raghavan, Louis Kraft, Fantin Mesny, Linda Rigerte:
A simple guide to de novo transcriptome assembly and annotation. - Liang Yu, Yujia Zheng, Lin Gao:
MiRNA-disease association prediction based on meta-paths. - Yong Lu, Gang Xue, Ningbo Zheng, Kun Han, Wenzhong Yang, Rui-Sheng Wang, Ling-Yun Wu, Lance D. Miller, Timothy Pardee, Pierre L. Triozzi, Hui-Wen Lo, Kounosuke Watabe, Stephen T. C. Wong, Boris C. Pasche, Wei Zhang, Guangxu Jin:
hDirect-MAP: projection-free single-cell modeling of response to checkpoint immunotherapy. - Yongsheng Li, Yunpeng Zhang, Tao Pan, Ping Zhou, Weiwei Zhou, Yueying Gao, Shaojiang Zheng, Juan Xu:
Shedding light on the hidden human proteome expands immunopeptidome in cancer. - Yue Su, Keyu Du, Jun Wang, Jin-Mao Wei, Jian Liu:
Multi-variable AUC for sifting complementary features and its biomedical application. - Josephine Yates, Valentina Boeva:
Deciphering the etiology and role in oncogenic transformation of the CpG island methylator phenotype: a pan-cancer analysis. - Charles E. Mordaunt, Julia S. Mouat, Rebecca J. Schmidt, Janine M. Lasalle:
Comethyl: a network-based methylome approach to investigate the multivariate nature of health and disease. - Ju Xiang, Jiashuai Zhang, Yichao Zhao, Fang-Xiang Wu, Min Li:
Biomedical data, computational methods and tools for evaluating disease-disease associations. - Ge Wang, Min-Qi Xue, Hong-Bin Shen, Ying-Ying Xu:
Learning protein subcellular localization multi-view patterns from heterogeneous data of imaging, sequence and networks. - Ran Su, Yixuan Huang, De-gan Zhang, Guobao Xiao, Leyi Wei:
SRDFM: Siamese Response Deep Factorization Machine to improve anti-cancer drug recommendation. - Gil Ben-Cohen, Flora Doffe, Michal Devir, Bernard Leroy, Thierry Soussi, Shai Rosenberg:
TP53_PROF: a machine learning model to predict impact of missense mutations in TP53. - Wenming Wu, Wensheng Zhang, Xiaoke Ma:
Network-based integrative analysis of single-cell transcriptomic and epigenomic data for cell types. - Yajie Meng, Changcheng Lu, Min Jin, Junlin Xu, Xiangxiang Zeng, Jialiang Yang:
A weighted bilinear neural collaborative filtering approach for drug repositioning. - Taeho Jo, Kwangsik Nho, Paula Bice, Andrew J. Saykin:
Deep learning-based identification of genetic variants: application to Alzheimer's disease classification. - Min Dai, Xiaobing Pei, Xiu-Jie Wang:
Accurate and fast cell marker gene identification with COSG. - Sazan Mahbub, Md. Shamsuzzoha Bayzid:
EGRET: edge aggregated graph attention networks and transfer learning improve protein-protein interaction site prediction. - Zhi Ruan, Shengmei Zou, Zeyu Wang, Luhan Zhang, Hangfei Chen, Yuye Wu, Huiqiong Jia, Mohamed S. Draz, Ye Feng:
Toward accurate diagnosis and surveillance of bacterial infections using enhanced strain-level metagenomic next-generation sequencing of infected body fluids. - Sören Richard Stahlschmidt, Benjamin Ulfenborg, Jane Synnergren:
Multimodal deep learning for biomedical data fusion: a review. - Shuang Wang, Tao Song, Shugang Zhang, Mingjian Jiang, Zhiqiang Wei, Zhen Li:
Molecular substructure tree generative model for de novo drug design. - Tingting Yang, Mingyu Gan, Qingyun Liu, Wenying Liang, Qiqin Tang, Geyang Luo, Tianyu Zuo, Yongchao Guo, Chuangyue Hong, Qibing Li, Weiguo Tan, Qian Gao:
SAM-TB: a whole genome sequencing data analysis website for detection of Mycobacterium tuberculosis drug resistance and transmission. - Rovshan G. Sadygov:
Protein turnover models for LC-MS data of heavy water metabolic labeling. - Vivek Bhakta Mathema, Kassaporn Duangkumpha, Kwanjeera Wanichthanarak, Narumol Jariyasopit, Esha Dhakal, Nuankanya Sathirapongsasuti, Chagriya Kitiyakara, Yongyut Sirivatanauksorn, Sakda Khoomrung:
CRISP: a deep learning architecture for GC × GC-TOFMS contour ROI identification, simulation and analysis in imaging metabolomics. - Bosheng Song, Xiaoyan Luo, Xiaoli Luo, Yuansheng Liu, Zhangming Niu, Xiangxiang Zeng:
Learning spatial structures of proteins improves protein-protein interaction prediction. - Corrado Pancotti, Silvia Benevenuta, Giovanni Birolo, Virginia Alberini, Valeria Repetto, Tiziana Sanavia, Emidio Capriotti, Piero Fariselli:
Predicting protein stability changes upon single-point mutation: a thorough comparison of the available tools on a new dataset. - John D. O'Connor, Ian M. Overton, Stephen J. McMahon:
RadSigBench: a framework for benchmarking functional genomics signatures of cancer cell radiosensitivity. - Xiaopeng Jin, Xiaoling Luo, Bin Liu:
Correction to: PHR-search: a search framework for protein remote homology detection based on the predicted protein hierarchical relationships. - Lu Jiang, Jiahao Sun, Yue Wang, Qiao Ning, Na Luo, Minghao Yin:
Identifying drug-target interactions via heterogeneous graph attention networks combined with cross-modal similarities. - Hui Tang, Xiangtian Yu, Rui Liu, Tao Zeng:
Vec2image: an explainable artificial intelligence model for the feature representation and classification of high-dimensional biological data by vector-to-image conversion. - Chen Shen, Huiyu Li, Miao Li, Yu Niu, Jing Liu, Li Zhu, Hongsheng Gui, Wei Han, Huiying Wang, Wenpei Zhang, Xiaochen Wang, Xiao Luo, Yu Sun, Jiangwei Yan, Fanglin Guan:
DLRAPom: a hybrid pipeline of Optimized XGBoost-guided integrative multiomics analysis for identifying targetable disease-related lncRNA-miRNA-mRNA regulatory axes. - Qisheng Pan, Thanh-Binh Nguyen, David B. Ascher, Douglas E. V. Pires:
Systematic evaluation of computational tools to predict the effects of mutations on protein stability in the absence of experimental structures. - Benedict Anchang, Raul Mendez-Giraldez, Xiaojiang Xu, Trevor K. Archer, Qing Chen, Guang Hu, Sylvia K. Plevritis, Alison Anne Motsinger-Reif, Jian-Liang Li:
Visualization, benchmarking and characterization of nested single-cell heterogeneity as dynamic forest mixtures. - Shanshan Cheng, Jingjing Lyu, Xian Shi, Kai Wang, Zengmiao Wang, Minghua Deng, Baoluo Sun, Chaolong Wang:
Rare variant association tests for ancestry-matched case-control data based on conditional logistic regression. - Zhaohui Zhan, Lusheng Wang:
Proteoform identification based on top-down tandem mass spectra with peak error corrections. - Qianmu Yuan, Sheng Chen, Jiahua Rao, Shuangjia Zheng, Huiying Zhao, Yuedong Yang:
AlphaFold2-aware protein-DNA binding site prediction using graph transformer. - Lei Li, Siriruk Changrob, Yanbin Fu, Olivia Stovicek, Jenna J. Guthmiller, Joshua J. C. McGrath, Haley L. Dugan, Christopher T. Stamper, Nai-Ying Zheng, Min Huang, Patrick C. Wilson:
Librator: a platform for the optimized analysis, design, and expression of mutable influenza viral antigens. - Alejandro Rubio, Juan Jiménez, Antonio J. Pérez-Pulido:
Assessment of selection pressure exerted on genes from complete pangenomes helps to improve the accuracy in the prediction of new genes. - Xiaoyu Wang, Fuyi Li, Jing Xu, Jia Rong, Geoffrey I. Webb, Zongyuan Ge, Jian Li, Jiangning Song:
ASPIRER: a new computational approach for identifying non-classical secreted proteins based on deep learning. - Dalang Yu, Xiao Yang, Bixia Tang, Yi-Hsuan Pan, Jianing Yang, Guangya Duan, Junwei Zhu, Zi-Qian Hao, Hailong Mu, Long Dai, Wangjie Hu, Mochen Zhang, Ying Cui, Tong Jin, Cui-Ping Li, Lina Ma, Xiao Su, Guoqing Zhang, Wenming Zhao, Haipeng Li:
Coronavirus GenBrowser for monitoring the transmission and evolution of SARS-CoV-2. - Mingmin Xu, Yuanyuan Chen, Zhihui Xu, Liangyun Zhang, Hangjin Jiang, Cong Pian:
MiRLoc: predicting miRNA subcellular localization by incorporating miRNA-mRNA interactions and mRNA subcellular localization. - Xiaopeng Jin, Xiaoling Luo, Bin Liu:
PHR-search: a search framework for protein remote homology detection based on the predicted protein hierarchical relationships. - Yuguo Zha, Kang Ning:
Ontology-aware neural network: a general framework for pattern mining from microbiome data. - Alexios Chatzigoulas, Zoe Cournia:
Predicting protein-membrane interfaces of peripheral membrane proteins using ensemble machine learning. - Yanglan Gan, Xingyu Huang, Guobing Zou, Shuigeng Zhou, Jihong Guan:
Deep structural clustering for single-cell RNA-seq data jointly through autoencoder and graph neural network. - Hyunsu An, Minho Eun, Jawoon Yi, Jihwan Park:
CRESSP: a comprehensive pipeline for prediction of immunopathogenic SARS-CoV-2 epitopes using structural properties of proteins. - Rui Cheng, Zhaochun Xu, Meng Luo, Pingping Wang, Huimin Cao, Xiyun Jin, Wenyang Zhou, Lixing Xiao, Qinghua Jiang:
Identification of alternative splicing-derived cancer neoantigens for mRNA vaccine development. - Junjie Wee, Kelin Xia:
Persistent spectral based ensemble learning (PerSpect-EL) for protein-protein binding affinity prediction. - Ting Wang, Jiahao Qiao, Shuo Zhang, Yongyue Wei, Ping Zeng:
Simultaneous test and estimation of total genetic effect in eQTL integrative analysis through mixed models. - Xiaotai Huang, Songwei Jia, Lin Gao, Jing Wu:
Reconstruction of human protein-coding gene functional association network based on machine learning. - Hongru Shen, Xilin Shen, Mengyao Feng, Dan Wu, Chao Zhang, Yichen Yang, Meng Yang, Jiani Hu, Jilei Liu, Wei Wang, Yang Li, Qiang Zhang, Jilong Yang, Kexin Chen, Xiangchun Li:
A universal approach for integrating super large-scale single-cell transcriptomes by exploring gene rankings. - Yanlun Tu, Houchao Lei, Hong-Bin Shen, Yang Yang:
SIFLoc: a self-supervised pre-training method for enhancing the recognition of protein subcellular localization in immunofluorescence microscopic images. - Rick Gelhausen, Teresa Müller, Sarah L. Svensson, Omer S. Alkhnbashi, Cynthia M. Sharma, Florian Eggenhofer, Rolf Backofen:
RiboReport - benchmarking tools for ribosome profiling-based identification of open reading frames in bacteria. - Chenjin Ma, Mengyun Wu, Shuangge Ma:
Analysis of cancer omics data: a selective review of statistical techniques. - Yijie Ding, Jijun Tang, Fei Guo, Quan Zou:
Identification of drug-target interactions via multiple kernel-based triple collaborative matrix factorization. - Siyu Han, Jialing Huang, Francesco Foppiano, Cornelia Prehn, Jerzy Adamski, Karsten Suhre, Ying Li, Giuseppe Matullo, Freimut Schliess, Christian Gieger, Annette Peters, Rui Wang-Sattler:
TIGER: technical variation elimination for metabolomics data using ensemble learning architecture. - Xiaohui Shi, Huajing Teng, Leisheng Shi, Wenjian Bi, Wenqing Wei, Fengbiao Mao, Zhongsheng Sun:
Comprehensive evaluation of computational methods for predicting cancer driver genes. - Yuning Yang, Zilong Hou, Yansong Wang, Hongli Ma, Pingping Sun, Zhiqiang Ma, Ka-Chun Wong, Xiangtao Li:
HCRNet: high-throughput circRNA-binding event identification from CLIP-seq data using deep temporal convolutional network. - Xianyu Xu, Ling Yue, Bingchun Li, Ying Liu, Yuan Wang, Wenjuan Zhang, Lin Wang:
DSGAT: predicting frequencies of drug side effects by graph attention networks. - Xiaobo Sun, Xiaochu Lin, Ziyi Li, Hao Wu:
A comprehensive comparison of supervised and unsupervised methods for cell type identification in single-cell RNA-seq. - Dejun Jiang, Huiyong Sun, Jike Wang, Chang-Yu Hsieh, Yuquan Li, Zhenxing Wu, Dong-Sheng Cao, Jian Wu, Tingjun Hou:
Out-of-the-box deep learning prediction of quantum-mechanical partial charges by graph representation and transfer learning. - Kaimiao Hu, Hui Cui, Tiangang Zhang, Chang Sun, Ping Xuan:
ALDPI: adaptively learning importance of multi-scale topologies and multi-modality similarities for drug-protein interaction prediction. - Xubo Tang, Jiayu Shang, Yanni Sun:
RdRp-based sensitive taxonomic classification of RNA viruses for metagenomic data. - Yaning Yang, Xiaoqi Wang, Deshan Zhou, Dong-Qing Wei, Shaoliang Peng:
SVPath: an accurate pipeline for predicting the pathogenicity of human exon structural variants. - Yiwei Meng, Yanhong Huang, Xiao Chang, Xiaoping Liu, Luonan Chen:
Transcriptome analysis method based on differential distribution evaluation. - Ya-Hui Zhou, Guo Li, Yuan-Ming Zhang:
A compressed variance component mixed model framework for detecting small and linked QTL-by-environment interactions. - Snehalika Lall, Abhik Ghosh, Sumanta Ray, Sanghamitra Bandyopadhyay:
sc-REnF: An entropy guided robust feature selection for single-cell RNA-seq data. - Ruolan Chen, Feng Xia, Bing Hu, Shuting Jin, Xiangrong Liu:
Drug-target interactions prediction via deep collaborative filtering with multiembeddings. - Xiaowen Wang, Hongming Zhu, Yizhi Jiang, Yulong Li, Chen Tang, Xiaohan Chen, Yunjie Li, Qi Liu, Qin Liu:
PRODeepSyn: predicting anticancer synergistic drug combinations by embedding cell lines with protein-protein interaction network. - Pedro Sánchez-Sánchez, Francisco-José Santonja, Alfonso Benítez-Páez:
Assessment of human microbiota stability across longitudinal samples using iteratively growing-partitioned clustering. - Sho Tsukiyama, Md. Mehedi Hasan, Hong-Wen Deng, Hiroyuki Kurata:
BERT6mA: prediction of DNA N6-methyladenine site using deep learning-based approaches. - Simone Marini, Rodrigo A. Mora, Christina Boucher, Noelle Robertson Noyes, Mattia Prosperi:
Towards routine employment of computational tools for antimicrobial resistance determination via high-throughput sequencing. - Ran Wang, Xubin Zheng, Jun Wang, Shibiao Wan, Fangda Song, Man Hon Wong, Kwong-Sak Leung, Lixin Cheng:
Improving bulk RNA-seq classification by transferring gene signature from single cells in acute myeloid leukemia. - Yaowen Chen, Zhen He, Yahui Men, Guohua Dong, Shuofeng Hu, Xiaomin Ying:
MetaLogo: a heterogeneity-aware sequence logo generator and aligner. - Guiying Wu, Xiangyu Li, Wenbo Guo, Zheng Wei, Tao Hu, Yiran Shan, Jin Gu:
JEBIN: analyzing gene co-expressions across multiple datasets by joint network embedding. - Nan Sheng, Lan Huang, Yan Wang, Jing Zhao, Ping Xuan, Ling Gao, Yangkun Cao:
Multi-channel graph attention autoencoders for disease-related lncRNAs prediction. - Vishakha Singh, Sameer Shrivastava, Sanjay Kumar Singh, Abhinav Kumar, Sonal Saxena:
Accelerating the discovery of antifungal peptides using deep temporal convolutional networks. - Elham Khalili, Shahin Ramazi, Faezeh Ghanati, Samaneh Kouchaki:
Predicting protein phosphorylation sites in soybean using interpretable deep tabular learning network. - Tangbo Zhong, Zhengwei Li, Zhu-Hong You, Ru Nie, Huan Zhao:
Predicting miRNA-disease associations based on graph random propagation network and attention network. - Laura Balagué-Dobón, Alejandro Cáceres, Juan R. González:
Fully exploiting SNP arrays: a systematic review on the tools to extract underlying genomic structure. - Jing Wang, Junfeng Xia, Dayu Tan, Rongxin Lin, Yansen Su, Chun-Hou Zheng:
scHFC: a hybrid fuzzy clustering method for single-cell RNA-seq data optimized by natural computation. - Chun-Chun Wang, Tian-Hao Li, Li Huang, Xing Chen:
Prediction of potential miRNA-disease associations based on stacked autoencoder. - Yunda Si, Yi Zhang, Chengfei Yan:
A reproducibility analysis-based statistical framework for residue-residue evolutionary coupling detection. - Mengyuan Zhao, Wenying He, Jijun Tang, Quan Zou, Fei Guo:
A hybrid deep learning framework for gene regulatory network inference from single-cell transcriptomic data. - Jinsong Shao, Qineng Gong, Zeyu Yin, Wenjie Pan, Sanjeevi Pandiyan, Li Wang:
S2DV: converting SMILES to a drug vector for predicting the activity of anti-HBV small molecules. - Fengcheng Li, Ying Zhou, Ying Zhang, Jiayi Yin, Yunqing Qiu, Jianqing Gao, Feng Zhu:
POSREG: proteomic signature discovered by simultaneously optimizing its reproducibility and generalizability. - Xiao-Chen Zhang, Jiacai Yi, Guo-Ping Yang, Chengkun Wu, Tingjun Hou, Dong-Sheng Cao:
ABC-Net: a divide-and-conquer based deep learning architecture for SMILES recognition from molecular images. - Shiva Dahal-Koirala, Gabriel Balaban, Ralf Stefan Neumann, Lonneke Scheffer, Knut Erik Aslaksen Lundin, Victor Greiff, Ludvig Magne Sollid, Shuo-Wang Qiao, Geir Kjetil Sandve:
TCRpower: quantifying the detection power of T-cell receptor sequencing with a novel computational pipeline calibrated by spike-in sequences. - Chengming Zhang, Yabin Chen, Tao Zeng, Chuanchao Zhang, Luonan Chen:
Deep latent space fusion for adaptive representation of heterogeneous multi-omics data. - Sheng Yang, Xiang Zhou:
PGS-server: accuracy, robustness and transferability of polygenic score methods for biobank scale studies. - Melivoia Rapti, Yassine Zouaghi, Jenny Meylan, Emmanuelle Ranza, Stylianos E. Antonarakis, Federico Andrea Santoni:
CoverageMaster: comprehensive CNV detection and visualization from NGS short reads for genetic medicine applications.
Volume 23, Number 3, May 2022
- Liangzhen Zheng, Jintao Meng, Kai Jiang, Haidong Lan, Zechen Wang, Mingzhi Lin, Weifeng Li, Hongwei Guo, Yanjie Wei, Yuguang Mu:
Improving protein-ligand docking and screening accuracies by incorporating a scoring function correction term. - Hongchao Ji, Xue Lu, Zhenxiang Zheng, Siyuan Sun, Chris Soon Heng Tan:
ProSAP: a GUI software tool for statistical analysis and assessment of thermal stability data. - Xiaocong Yang, Yang Liu, Jianhong Gan, Zhixiong Xiao, Yang Cao:
FitDock: protein-ligand docking by template fitting. - Mingsheng Tang, Tingting Hou, Xiaoran Tong, Xiaoxi Shen, Xuefen Zhang, Tong Wang, Qing Lu:
Fast heritability estimation based on MINQUE and batch training. - Ziwei Chen, Fuzhou Gong, Lin Wan, Liang Ma:
BiTSC 2: Bayesian inference of tumor clonal tree by joint analysis of single-cell SNV and CNA data. - Peng Zhou, Li Wen, Jing Lin, Li Mei, Qian Liu, Shuyong Shang, Juelin Li, Jianping Shu:
Integrated unsupervised-supervised modeling and prediction of protein-peptide affinities at structural level. - Likun Jiang, Changzhi Jiang, Xinyu Yu, Rao Fu, Shuting Jin, Xiangrong Liu:
DeepTTA: a transformer-based model for predicting cancer drug response. - Zhonghua Jiang, Yongmei Lu, Zhuochong Liu, Wei Wu, Xinyi Xu, András Dinnyés, Zhonghua Yu, Li Chen, Qun Sun:
Drug resistance prediction and resistance genes identification in Mycobacterium tuberculosis based on a hierarchical attentive neural network utilizing genome-wide variants. - Meidan Zhao, Pengqian Wang, Kai Zhang:
Bioinformatics/network topology analysis of acupuncture in the treatment of COVID-19: response to methodological issues. - Zhihua Du, Yang-Han Wu, Yu-An Huang, Jie Chen, Gui-Qing Pan, Lun Hu, Zhu-Hong You, Jian-Qiang Li:
GraphTGI: an attention-based graph embedding model for predicting TF-target gene interactions. - Cheng-Hong Yang, Yu-Huei Cheng, Li-Yeh Chuang, Yu-Da Lin:
Multiobjective optimization-driven primer design mechanism: towards user-specified parameters of PCR primer. - Ping Xuan, Zhe Gong, Hui Cui, Bochong Li, Tiangang Zhang:
Fully connected autoencoder and convolutional neural network with attention-based method for inferring disease-related lncRNAs. - Jing Wang, Qinglong Zhang, Junshan Han, Yanpeng Zhao, Caiyun Zhao, Bowei Yan, Chong Dai, Lianlian Wu, Yuqi Wen, Yixin Zhang, Dongjin Leng, Zhongming Wang, Xiaoxi Yang, Song He, Xiaochen Bo:
Computational methods, databases and tools for synthetic lethality prediction. - Fuxu Wang, Haoyan Wang, Lizhuang Wang, Haoyu Lu, Shizheng Qiu, Tianyi Zang, Xinjun Zhang, Yang Hu:
MHCRoBERTa: pan-specific peptide-MHC class I binding prediction through transfer learning with label-agnostic protein sequences. - Wenjing Ma, Sumeet Sharma, Peng Jin, Shannon L. Gourley, Zhaohui S. Qin:
LRcell: detecting the source of differential expression at the sub-cell-type level from bulk RNA-seq data. - Shengpeng Yu, Hong Wang, Tianyu Liu, Cheng Liang, Jiawei Luo:
A knowledge-driven network for fine-grained relationship detection between miRNA and disease. - Xingyu Li, Xue Lin, Xueyin Mei, Pin Chen, Anna Liu, Weicheng Liang, Shan Chang, Jian Li:
HLA3D: an integrated structure-based computational toolkit for immunotherapy. - Qiongqiong Feng, Minghua Hou, Jun Liu, Kai-Long Zhao, Guijun Zhang:
Construct a variable-length fragment library for de novo protein structure prediction. - Mariella Bonomo, Raffaele Giancarlo, Daniele Greco, Simona E. Rombo:
Topological ranks reveal functional knowledge encoded in biological networks: a comparative analysis. - Ying Yang, Sha Tian, Yushan Qiu, Pu Zhao, Quan Zou:
MDICC: novel method for multi-omics data integration and cancer subtype identification. - Xue Zhang, Chen-Guang Liu, Shi-Hui Yang, Xia Wang, Feng-Wu Bai, Zhuo Wang:
Benchmarking of long-read sequencing, assemblers and polishers for yeast genome. - Ye Hong, Dani Flinkman, Tomi Suomi, Sami Pietilä, Peter James, Eleanor Coffey, Laura L. Elo:
Correction to: PhosPiR: an automated phosphoproteomic pipeline in R. - Sare Amerifar, Mohammad Norouzi, Mahmoud Ghandi:
A tool for feature extraction from biological sequences. - Zimo Huang, Jun Wang, Zhongmin Yan, Maozu Guo:
Differentially expressed genes prediction by multiple self-attention on epigenetics data. - César R. García-Jacas, Sergio Alejandro Pinacho-Castellanos, Luis A. García-González, Carlos A. Brizuela:
Do deep learning models make a difference in the identification of antimicrobial peptides? - Lei Wang, Yaqin Tan, Xiaoyu Yang, Linai Kuang, Pengyao Ping:
Review on predicting pairwise relationships between human microbes, drugs and diseases: from biological data to computational models. - Anna Dal Molin, Enrico Gaffo, Valeria Difilippo, Alessia Buratin, Caterina Tretti Parenzan, Silvia Bresolin, Stefania Bortoluzzi:
Correction to: CRAFT a bioinformatics software for custom prediction of circular RNA functions. - Yaowen Gu, Si Zheng, Zidu Xu, Qijin Yin, Liang Li, Jiao Li:
An efficient curriculum learning-based strategy for molecular graph learning. - Axel Kowald, Israel Barrantes, Steffen Möller, Daniel Palmer, Hugo Murua Escobar, Anne Schwerk, Georg Fuellen:
Transfer learning of clinical outcomes from preclinical molecular data, principles and perspectives. - Sarah Mubeen, Alpha Tom Kodamullil, Martin Hofmann-Apitius, Daniel Domingo-Fernández:
On the influence of several factors on pathway enrichment analysis. - Yi-Yang Feng, Hui Yu, Yue-Hua Feng, Jian-Yu Shi:
Directed graph attention networks for predicting asymmetric drug-drug interactions. - Ju Xiang, Xiangmao Meng, Yichao Zhao, Fang-Xiang Wu, Min Li:
HyMM: hybrid method for disease-gene prediction by integrating multiscale module structure. - Gang Xu, Qinghua Wang, Jianpeng Ma:
Expression of Concern to: OPUS-Rota4: a gradient-based protein side-chain modeling framework assisted by deep learning-based predictors. - Ettayapuram Ramaprasad Azhagiya Singam, Martyn T. Smith, David McCoy, Alan E. Hubbard, Michele A. La Merrill, Kathleen A. Durkin:
Predicting the binding of small molecules to nuclear receptors using machine learning. - Zhenxing Wu, Dejun Jiang, Jike Wang, Xujun Zhang, Hongyan Du, Lurong Pan, Chang-Yu Hsieh, Dongsheng Cao, Tingjun Hou:
Knowledge-based BERT: a method to extract molecular features like computational chemists. - Rashmie Abeysinghe, Yuntao Yang, Mason Bartels, W. Jim Zheng, Licong Cui:
An evidence-based lexical pattern approach for quality assurance of Gene Ontology relations. - Rakesh Kaundal, Cristian D. Loaiza, Naveen Duhan, Nicholas Flann:
deepHPI: a comprehensive deep learning platform for accurate prediction and visualization of host-pathogen protein-protein interactions. - Xiaozhe Wan, Xiaolong Wu, Dingyan Wang, Xiaoqin Tan, Xiaohong Liu, Zunyun Fu, Hualiang Jiang, Mingyue Zheng, Xutong Li:
An inductive graph neural network model for compound-protein interaction prediction based on a homogeneous graph. - Yidi Deng, Jarny Choi, Kim-Anh Lê Cao:
Sincast: a computational framework to predict cell identities in single-cell transcriptomes using bulk atlases as references. - Qiang Kang, Jun Meng, Yushi Luan:
RNAI-FRID: novel feature representation method with information enhancement and dimension reduction for RNA-RNA interaction. - Mengya Liu, Zhan-Li Sun, Zhigang Zeng, Kin-Man Lam:
MGF6mARice: prediction of DNA N6-methyladenine sites in rice by exploiting molecular graph feature and residual block. - Han-Jing Jiang, Yabing Huang, Qianpeng Li:
Spectral clustering of single cells using Siamese nerual network combined with improved affinity matrix. - Ping Xuan, Meng Wang, Yong Liu, Dong Wang, Tiangang Zhang, Toshiya Nakaguchi:
Integrating specific and common topologies of heterogeneous graphs and pairwise attributes for drug-related side effect prediction. - Talal Ahmed, Mark A. Carty, Stephane Wenric, Jonathan R. Dry, Ameen A. Salahudeen, Aly A. Khan, Eric Lefkofsky, Martin C. Stumpe, Raphael Pelossof:
Privacy preserving validation for multiomic prediction models. - Han-Yuan Zhang, Lei Wang, Zhu-Hong You, Lun Hu, Bo-Wei Zhao, Zheng-Wei Li, Yang-Ming Li:
iGRLCDA: identifying circRNA-disease association based on graph representation learning. - Liang Yu, Yujia Zheng, Bingyi Ju, Chunyan Ao, Lin Gao:
Research progress of miRNA-disease association prediction and comparison of related algorithms. - Mengya Chai, Yajuan Guo, Liu Yang, Jianhui Li, Shuo Liu, Lei Chen, Yuelei Shen, Yi Yang, Youchun Wang, Lida Xu, Changyuan Yu:
A high-throughput single cell-based antibody discovery approach against the full-length SARS-CoV-2 spike protein suggests a lack of neutralizing antibodies targeting the highly conserved S2 domain. - Yueming Yin, Haifeng Hu, Zhen Yang, Feihu Jiang, Yihe Huang, Jiansheng Wu:
AFSE: towards improving model generalization of deep graph learning of ligand bioactivities targeting GPCR proteins. - Liwen Xu, Shiwei Zhu, Yujia Lan, Min Yan, Zedong Jiang, Jiali Zhu, Gaoming Liao, Yanyan Ping, Jinyuan Xu, Bo Pang, Yunpeng Zhang, Yun Xiao, Xia Li:
Revealing the contribution of somatic gene mutations to shaping tumor immune microenvironment. - Anna Torkamannia, Yadollah Omidi, Reza Ferdousi:
A review of machine learning approaches for drug synergy prediction in cancer. - Xiaoyi Guo, Yizhang Jiang, Quan Zou:
Structured Sparse Regularized TSK Fuzzy System for predicting therapeutic peptides. - Dafei Xie, Song He, Lu Han, Lianlian Wu, Hai Huang, Huan Tao, Pingkun Zhou, Xunlong Shi, Hui Bai, Xiaochen Bo:
Systematic optimization of host-directed therapeutic targets and preclinical validation of repositioned antiviral drugs. - Naveen Duhan, Jeanette M. Norton, Rakesh Kaundal:
deepNEC: a novel alignment-free tool for the identification and classification of nitrogen biochemical network-related enzymes using deep learning. - Haohuai He, Guanxing Chen, Calvin Yu-Chian Chen:
3DGT-DDI: 3D graph and text based neural network for drug-drug interaction prediction. - Tao Wang, Yongzhuang Liu, Quanwei Yin, Jiaquan Geng, Jin Chen, Xipeng Yin, Yongtian Wang, Xuequn Shang, Chunwei Tian, Yadong Wang, Jiajie Peng:
Correction to: Enhancing discoveries of molecular QTL studies with small sample size using summary statistic imputation. - Liheng Luo, Michael Gribskov, Sufang Wang:
Bibliometric review of ATAC-Seq and its application in gene expression. - Yu Liu:
scDeconv: an R package to deconvolve bulk DNA methylation data with scRNA-seq data and paired bulk RNA-DNA methylation data. - Paul R. Buckley, Chloe H. Lee, Ruichong Ma, Isaac Woodhouse, Jeongmin Woo, Vasily Tsvetkov, Dmitrii S. Shcherbinin, Agne Antanaviciute, Mikhail Shughay, Margarida Rei, Alison Simmons, Hashem Koohy:
Evaluating performance of existing computational models in predicting CD8+ T cell pathogenic epitopes and cancer neoantigens. - Wenjian Xu, Haochen He, Zhengguang Guo, Wei Li:
Evaluation of machine learning models on protein level inference from prioritized RNA features. - Wajdi Alghamdi, Muhammad Attique, Ebraheem Alzahrani, Malik Zaka Ullah, Yaser Daanial Khan:
LBCEPred: a machine learning model to predict linear B-cell epitopes. - Xuanjin Cheng, Yongxing Liu, Jiahe Wang, Yujie Chen, Andrew Gordon Robertson, Xuekui Zhang, Steven J. M. Jones, Stefan Taubert:
cSurvival: a web resource for biomarker interactions in cancer outcomes and in cell lines. - Md. Solayman, Thomas Litfin, Jaswinder Singh, Kuldip K. Paliwal, Yaoqi Zhou, Jian Zhan:
Probing RNA structures and functions by solvent accessibility: an overview from experimental and computational perspectives. - Kanghao Shao, Yunhao Zhang, Yuqi Wen, Zhongnan Zhang, Song He, Xiaochen Bo:
DTI-HETA: prediction of drug-target interactions based on GCN and GAT on heterogeneous graph. - Yuanyuan Ma, Zexuan Sun, Pengcheng Zeng, Wenyu Zhang, Zhixiang Lin:
JSNMF enables effective and accurate integrative analysis of single-cell multiomics data. - Matteo Tiberti, Thilde Terkelsen, Kristine Degn, Ludovica Beltrame, Tycho Canter Cremers, Isabelle da Piedade, Miriam Di Marco, Emiliano Maiani, Elena Papaleo:
MutateX: an automated pipeline for in silico saturation mutagenesis of protein structures and structural ensembles. - Yongqing Zhang, Qiang Zhang, Jiliu Zhou, Quan Zou:
A survey on the algorithm and development of multiple sequence alignment. - Mingsheng Tang, Tong Wang, Xuefen Zhang:
A review of SNP heritability estimation methods. - Renan Andrades, Mariana Recamonde Mendoza:
Machine learning methods for prediction of cancer driver genes: a survey paper. - Dietmar Fernández-Orth, Manuel Rueda, Babita Singh, Mauricio Moldes, Aina Jene, Marta Ferri, Claudia Vasallo, Lauren A. Fromont, Arcadi Navarro, Jordi Rambla:
A quality control portal for sequencing data deposited at the European genome-phenome archive. - Xiao-Rui Su, Lun Hu, Zhuhong You, Pengwei Hu, Bo-Wei Zhao:
Attention-based Knowledge Graph Representation Learning for Predicting Drug-drug Interactions. - Xia-an Bi, Wenyan Zhou, Sheng Luo, Yuhua Mao, Xi Hu, Bin Zeng, Luyun Xu:
Feature aggregation graph convolutional network based on imaging genetic data for diagnosis and pathogeny identification of Alzheimer's disease. - Miao-Hsia Lin, Pei-Shan Wu, Tzu-Hsuan Wong, I-Ying Lin, Johnathan Lin, Jürgen Cox, Sung-Huan Yu:
Benchmarking differential expression, imputation and quantification methods for proteomics data. - Yanqing Yang, Deshan Zhou, Xinben Zhang, Yulong Shi, Jiaxin Han, Liping Zhou, Leyun Wu, Minfei Ma, Jintian Li, Shaoliang Peng, Zhijian Xu, Weiliang Zhu:
D3AI-CoV: a deep learning platform for predicting drug targets and for virtual screening against COVID-19. - Zi-Yi Yang, Zhaofeng Ye, Yijia Xiao, Chang-Yu Hsieh, Sheng-Yu Zhang:
SPLDExtraTrees: robust machine learning approach for predicting kinase inhibitor resistance. - Ruibo Zhang, Souparno Ghosh, Ranadip Pal:
Predicting binding affinities of emerging variants of SARS-CoV-2 using spike protein sequencing data: observations, caveats and recommendations. - Fei Liu, Monika Heiner, David R. Gilbert:
Hybrid modelling of biological systems: current progress and future prospects. - Jing Liu, Xinghua Tang, Shuanglong Cui, Xiao Guan:
Predicting the function of rice proteins through Multi-instance Multi-label Learning based on multiple features fusion. - Wei Liu, Hui Lin, Li Huang, Li Peng, Ting Tang, Qi Zhao, Li Yang:
Identification of miRNA-disease associations via deep forest ensemble learning based on autoencoder. - Xiaoman Wang, Lifei Ma, Xiaoya Pei, Heping Wang, Xiaoqiang Tang, Jian-Fei Pei, Yang-Nan Ding, Siyao Qu, Zi-Yu Wei, Hui-Yu Wang, Xiaoyue Wang, Gong-Hong Wei, De-Pei Liu, Houzao Chen:
Comprehensive assessment of cellular senescence in the tumor microenvironment. - Rongfan Tang, Pengcheng Chen, Zhe Wang, Lingling Wang, Haiping Hao, Tingjun Hou, Huiyong Sun:
Characterizing the stabilization effects of stabilizers in protein-protein systems with end-point binding free energy calculations. - Ping Xuan, Xiangfeng Meng, Ling Gao, Tiangang Zhang, Toshiya Nakaguchi:
Heterogeneous multi-scale neighbor topologies enhanced drug-disease association prediction. - Anqi Lin, Chang Qi, Ting Wei, Mengyao Li, Quan Cheng, Zaoqu Liu, Peng Luo, Jian Zhang:
CAMOIP: a web server for comprehensive analysis on multi-omics of immunotherapy in pan-cancer. - Yusong Liu, Tongxin Wang, Ben Duggan, Michael F. Sharpnack, Kun Huang, Jie Zhang, Xiufen Ye, Travis S. Johnson:
SPCS: a spatial and pattern combined smoothing method for spatial transcriptomic expression. - Eric Van Buren, Ming Hu, Liang Cheng, John Wrobel, Kirk Wilhelmsen, Lishan Su, Yun Li, Di Wu:
TWO-SIGMA-G: a new competitive gene set testing framework for scRNA-seq data accounting for inter-gene and cell-cell correlation. - Xia-an Bi, Lou Li, Zizheng Wang, Yu Wang, Xun Luo, Luyun Xu:
IHGC-GAN: influence hypergraph convolutional generative adversarial network for risk prediction of late mild cognitive impairment based on imaging genetic data. - Xiaoyu Guan, Yuqin Wang, Wei Shao, Zhongnian Li, Shuo Huang, Daoqiang Zhang:
S2Snet: deep learning for low molecular weight RNA identification with nanopore. - Fei Wu, Yao-Zhong Liu, Binhua Ling:
MTD: a unique pipeline for host and meta-transcriptome joint and integrative analyses of RNA-seq data. - Keilash Chirom, Md. Zubbair Malik, Irengbam Rocky Mangangcha, Pallavi Somvanshi, R. K. Brojen Singh:
Network medicine in ovarian cancer: topological properties to drug discovery. - Yachen Liu, Yalan Lin, Wenxian Yang, Yuxiang Lin, Yujuan Wu, Zheyang Zhang, Nuoqi Lin, Xianlong Wang, Mengsha Tong, Rongshan Yu:
Application of individualized differential expression analysis in human cancer proteome. - Yifan Shang, Xiucai Ye, Yasunori Futamura, Liang Yu, Tetsuya Sakurai:
Multiview network embedding for drug-target Interactions prediction by consistent and complementary information preserving. - Haiyue Wang, Xiaoke Ma:
Learning deep features and topological structure of cells for clustering of scRNA-sequencing data. - Yulian Ding, Xiujuan Lei, Bo Liao, Fang-Xiang Wu:
MLRDFM: a multi-view Laplacian regularized DeepFM model for predicting miRNA-disease associations. - Muhammad Toseef, Xiangtao Li, Ka-Chun Wong:
Reducing healthcare disparities using multiple multiethnic data distributions with fine-tuning of transfer learning. - Zuguang Gu, Daniel Hübschmann:
Improve consensus partitioning via a hierarchical procedure. - Martin Bartas, Adriana Volná, Christopher A. Beaudoin, Ebbe Toftgaard Poulsen, Jirí Cerven, Václav Brázda, Vladimír Spunda, Tom L. Blundell, Petr Pecinka:
Unheeded SARS-CoV-2 proteins? A deep look into negative-sense RNA. - Weizhong Zhao, Shujie Luo, Haifang Wu, Xingpeng Jiang, Tingting He, Xiaohua Hu:
A multi-label learning framework for predicting antibiotic resistance genes via dual-view modeling. - Yixin Yan, Mengyun Yang, Haochen Zhao, Guihua Duan, Xiaoqing Peng, Jianxin Wang:
Drug repositioning based on multi-view learning with matrix completion. - Amelia Villegas-Morcillo, Angel M. Gomez, Victoria E. Sánchez:
An analysis of protein language model embeddings for fold prediction. - Jing Li, Ya-Nan Wu, Sen Zhang, Xiao-Ping Kang, Tao Jiang:
Deep learning based on biologically interpretable genome representation predicts two types of human adaptation of SARS-CoV-2 variants. - Le Huang, Yuchen Yang, Gang Li, Minzhi Jiang, Jia Wen, Armen Abnousi, Jonathan D. Rosen, Ming Hu, Yun Li:
A systematic evaluation of Hi-C data enhancement methods for enhancing PLAC-seq and HiChIP data. - Wenze Ding, Kenta Nakai, Haipeng Gong:
Protein design via deep learning. - Xiangwen Wang, Yonggang Lu, Xianghong Lin:
Heterogeneous cryo-EM projection image classification using a two-stage spectral clustering based on novel distance measures.
Volume 23, Number 4, July 2022
- Zhi-Hui Luo, Lida Zhu, Ya-Min Wang, Sheng Hu Qian, Menglu Li, Wen Zhang, Zhen-Xia Chen:
DSEATM: drug set enrichment analysis uncovering disease mechanisms by biomedical text mining. - Saritha Kodikara, Susan Ellul, Kim-Anh Lê Cao:
Statistical challenges in longitudinal microbiome data analysis. - Artem Danilevsky, Avital Luba Polsky, Noam Shomron:
Adaptive sequencing using nanopores and deep learning of mitochondrial DNA. - Shangbo Ning, Huiwen Wang, Chen Zeng, Yunjie Zhao:
Prediction of allosteric druggable pockets of cyclin-dependent kinases. - Daniel N. Sosa, Russ B. Altman:
Contexts and contradictions: a roadmap for computational drug repurposing with knowledge inference. - Leandro A Bugnon, Alejandro A. Edera, Santiago Prochetto, Matias Gerard, Jonathan Raad, Emilio Fenoy, Mariano Rubiolo, Uciel Chorostecki, Toni Gabaldón, Federico Ariel, Leandro E. Di Persia, Diego H. Milone, Georgina Stegmayer:
Secondary structure prediction of long noncoding RNA: review and experimental comparison of existing approaches. - Xiao-Shuang Li, Xiang Liu, Le Lu, Xian-Sheng Hua, Ying Chi, Kelin Xia:
Multiphysical graph neural network (MP-GNN) for COVID-19 drug design. - Polina Suter, Jack Kuipers, Niko Beerenwinkel:
Discovering gene regulatory networks of multiple phenotypic groups using dynamic Bayesian networks. - Jun Bai, Renbo Tan, Zheng An, Ying Xu:
Quantitative estimation of intracellular oxidative stress in human tissues. - Wenxiang Zhang, Hang Wei, Bin Liu:
idenMD-NRF: a ranking framework for miRNA-disease association identification. - Qian Gao, Yu Zhang, Hongwei Sun, Tong Wang:
Evaluation of propensity score methods for causal inference with high-dimensional covariates. - Jianfeng Li, Benben Miao, Shixiang Wang, Wei Dong, Houshi Xu, Chenchen Si, Wei Wang, Songqi Duan, Jiacheng Lou, Zhiwei Bao, Hailuan Zeng, Zengzeng Yang, Wenyan Cheng, Fei Zhao, Jianming Zeng, Xue-Song Liu, Renxie Wu, Yang Shen, Zhu Chen, Saijuan Chen, Mingjie Wang:
Hiplot: a comprehensive and easy-to-use web service for boosting publication-ready biomedical data visualization. - Dat Thanh Nguyen, Quan Hoang Nguyen, Nguyen Thuy Duong, Nam S. Vo:
LmTag: functional-enrichment and imputation-aware tag SNP selection for population-specific genotyping arrays. - Tong Yang, Feng Gao:
High-quality pan-genome of Escherichia coli generated by excluding confounding and highly similar strains reveals an association between unique gene clusters and genomic islands. - Xingxin Pan, L. Frank Huang:
Multi-omics to characterize the functional relationships of R-loops with epigenetic modifications, RNAPII transcription and gene expression. - Xin Dong, Ke Chen, Wenbo Chen, Jun Wang, Liuping Chang, Jin Deng, Lei Wei, Leng Han, Chunhua Huang, Chunjiang He:
circRIP: an accurate tool for identifying circRNA-RBP interactions. - Jianhua Dai, Chao Jiang, Ruoyao Peng, Daojian Zeng, Yangding Li:
Chinese medical dialogue information extraction via contrastive multi-utterance inference. - Anna Niarakis, Dagmar Waltemath, James A. Glazier, Falk Schreiber, Sarah M. Keating, David P. Nickerson, Claudine Chaouiya, Anne Siegel, Vincent Noël, Henning Hermjakob, Tomás Helikar, Sylvain Soliman, Laurence Calzone:
Addressing barriers in comprehensiveness, accessibility, reusability, interoperability and reproducibility of computational models in systems biology. - Robson Bonidia, Anderson P. Avila-Santos, Breno Lívio Silva de Almeida, Peter F. Stadler, Ulisses Nunes da Rocha, Danilo Sipoli Sanches, André C. P. L. F. de Carvalho:
BioAutoML: automated feature engineering and metalearning to predict noncoding RNAs in bacteria. - Hyunho Kim, Minsu Park, Ingoo Lee, Hojung Nam:
BayeshERG: a robust, reliable and interpretable deep learning model for predicting hERG channel blockers. - Xinxin Peng, Xiaoyu Wang, Yuming Guo, Zongyuan Ge, Fuyi Li, Xin Gao, Jiangning Song:
RBP-TSTL is a two-stage transfer learning framework for genome-scale prediction of RNA-binding proteins. - Dezhong Lv, Kang Xu, Changbo Yang, Yujie Liu, Ya Luo, Weiwei Zhou, Haozhe Zou, Yangyang Cai, Na Ding, Xia Li, Tingting Shao, Yongsheng Li, Juan Xu:
PRES: a webserver for decoding the functional perturbations of RNA editing sites. - Jiayu Shang, Xubo Tang, Ruocheng Guo, Yanni Sun:
Accurate identification of bacteriophages from metagenomic data using Transformer. - Jiawen Chen, Weifang Liu, Tianyou Luo, Zhentao Yu, Minzhi Jiang, Jia Wen, Gaorav P. Gupta, Paola Giusti, Hongtu Zhu, Yuchen Yang, Yun Li:
A comprehensive comparison on cell-type composition inference for spatial transcriptomics data. - Hongyu Luo, Yingfei Xiang, Xiaomin Fang, Wei Li, Fan Wang, Hua Wu, Haifeng Wang:
BatchDTA: implicit batch alignment enhances deep learning-based drug-target affinity estimation. - Zijie Sun, Qinlai Huang, Yuhe Yang, Shihao Li, Hao Lv, Yang Zhang, Hao Lin, Lin Ning:
PSnoD: identifying potential snoRNA-disease associations based on bounded nuclear norm regularization. - Xinghu Qin, Charleston W. K. Chiang, Oscar E. Gaggiotti:
KLFDAPC: a supervised machine learning approach for spatial genetic structure analysis. - Tianjiao Zhang, Liang Chen, Rongzhen Li, Ning Liu, Xiaobing Huang, Garry Wong:
PIWI-interacting RNAs in human diseases: databases and computational models. - Weikang Gong, Junjie Wee, Min-Chun Wu, Xiaohan Sun, Chunhua Li, Kelin Xia:
Persistent spectral simplicial complex-based machine learning for chromosomal structural analysis in cellular differentiation. - Zhao Chen, Yin Jiang, Xiaoyu Zhang, Rui Zheng, Ruijin Qiu, Yang Sun, Chen Zhao, Hongcai Shang:
The prediction approach of drug-induced liver injury: response to the issues of reproducible science of artificial intelligence in real-world applications. - Varuni Sarwal, Sebastian Niehus, Ram Ayyala, Minyoung Kim, Aditya Sarkar, Sei Chang, Angela Lu, Neha Rajkumar, Nicholas Darci-Maher, Russell Littman, Karishma Chhugani, Arda Soylev, Zoia Comarova, Emily E. Wesel, Jacqueline Castellanos, Rahul Chikka, Margaret G. Distler, Eleazar Eskin, Jonathan Flint, Serghei Mangul:
A comprehensive benchmarking of WGS-based deletion structural variant callers. - Ryuji Hamamoto, Ken Takasawa, Hidenori Machino, Kazuma Kobayashi, Satoshi Takahashi, Amina Bolatkan, Norio Shinkai, Akira Sakai, Rina Aoyama, Masayoshi Yamada, Ken Asada, Masaaki Komatsu, Koji Okamoto, Hirokazu Kameoka, Syuzo Kaneko:
Application of non-negative matrix factorization in oncology: one approach for establishing precision medicine. - Fengcheng Li, Jiayi Yin, Mingkun Lu, Qingxia Yang, Zhenyu Zeng, Bing Zhang, Zhaorong Li, Yunqing Qiu, Haibin Dai, Yuzong Chen, Feng Zhu:
ConSIG: consistent discovery of molecular signature from OMIC data. - Nansu Zong, Ning Li, Andrew Wen, Victoria Ngo, Yue Yu, Ming Huang, Shaika Chowdhury, Chao Jiang, Sunyang Fu, Richard Weinshilboum, Guoqian Jiang, Lawrence Hunter, Hongfang Liu:
BETA: a comprehensive benchmark for computational drug-target prediction. - Xiaqiong Wang, Yalu Wen:
A penalized linear mixed model with generalized method of moments for prediction analysis on high-dimensional multi-omics data. - Teng Zhang, Shao-Wu Zhang, Jian Feng, Bei Zhang:
m 6 Aexpress-BHM: predicting m6A regulation of gene expression in multiple-groups context by a Bayesian hierarchical mixture model. - Raquel Pagano-Márquez, José Córdoba-Caballero, Beatriz Martínez-Poveda, Ana R. Quesada, Elena Rojano, Pedro Seoane, Juan A. G. Ranea, Miguel Ángel Medina:
Deepening the knowledge of rare diseases dependent on angiogenesis through semantic similarity clustering and network analysis. - Ruohan Wang, Yuwei Zhang, Mengbo Wang, Xikang Feng, Jianping Wang, Shuai Cheng Li:
Resolving single-cell copy number profiling for large datasets. - Zhichao Liu, Ting Li, Skylar Connor, Shraddha Thakkar, Ruth Roberts, Weida Tong:
Best practice and reproducible science are required to advance artificial intelligence in real-world applications. - Pietro Di Lena, Claudia Sala, Christine Nardini:
Evaluation of different computational methods for DNA methylation-based biological age. - Teemu J. Rintala, Arindam Ghosh, Vittorio Fortino:
Network approaches for modeling the effect of drugs and diseases. - Madhu Sharma, Indra Prakash Jha, Smriti Chawla, Neetesh Pandey, Omkar Chandra, Shreya Mishra, Vibhor Kumar:
Associating pathways with diseases using single-cell expression profiles and making inferences about potential drugs. - Anthony Huffman, Edison Ong, Junguk Hur, Adonis D'mello, Hervé Tettelin, Yongqun He:
COVID-19 vaccine design using reverse and structural vaccinology, ontology-based literature mining and machine learning. - Ning Wang, Ke Yan, Jun Zhang, Bin Liu:
iDRNA-ITF: identifying DNA- and RNA-binding residues in proteins based on induction and transfer framework. - Boris Vishnepolsky, Maya Grigolava, Grigol Managadze, Andrei Gabrielian, Alex Rosenthal, Darrell E. Hurt, Michael Tartakovsky, Malak Pirtskhalava:
Comparative analysis of machine learning algorithms on the microbial strain-specific AMP prediction. - Feiyue Sun, Jianqiang Sun, Qi Zhao:
A deep learning method for predicting metabolite-disease associations via graph neural network. - Zhi-Zheng Wang, Ming-Shu Wang, Fan Wang, Xing-Xing Shi, Wei Huang, Ge-Fei Hao, Guangfu Yang:
Exploring the kinase-inhibitor fragment interaction space facilitates the discovery of kinase inhibitor overcoming resistance by mutations. - Jessica Gliozzo, Marco Mesiti, Marco Notaro, Alessandro Petrini, Alex Patak, Antonio Puertas Gallardo, Alberto Paccanaro, Giorgio Valentini, Elena Casiraghi:
Heterogeneous data integration methods for patient similarity networks. - Mehdi Yazdani-Jahromi, Niloofar Yousefi, Aida Tayebi, Elayaraja Kolanthai, Craig J. Neal, Sudipta Seal, Özlem Özmen Garibay:
AttentionSiteDTI: an interpretable graph-based model for drug-target interaction prediction using NLP sentence-level relation classification. - Haiping Zhang, Konda Mani Saravanan, Yang Yang, Yanjie Wei, Yi Pan, John Z. H. Zhang:
Generating and screening de novo compounds against given targets using ultrafast deep learning models as core components. - Mengfei Liu, Linlin Hao, Sien Yang, Xiaohui Wu:
PolyAtailor: measuring poly(A) tail length from short-read and long-read sequencing data. - Qiang Wei, Chao Jin, Yang Wang, Shanshan Guo, Xu Guo, Xiaonan Liu, Jiaze An, Jinliang Xing, Bingshan Li:
A computational framework to unify orthogonal information in DNA methylation and copy number aberrations in cell-free DNA for early cancer detection. - Pietro Hiram Guzzi, Francesco Petrizzelli, Tommaso Mazza:
Disease spreading modeling and analysis: a survey. - Emilio Fenoy, Alejandro A. Edera, Georgina Stegmayer:
Transfer learning in proteins: evaluating novel protein learned representations for bioinformatics tasks. - Qifan He, Jian Yang, Yonghai Jin:
Immune infiltration and clinical significance analyses of the coagulation-related genes in hepatocellular carcinoma. - Grete Francesca Privitera, Salvatore Alaimo, Alfredo Ferro, Alfredo Pulvirenti:
Virus finding tools: current solutions and limitations. - Wiktoria Wilman, Sonia Wróbel, Weronika Bielska, Piotr Deszynski, Pawel Dudzic, Igor Jaszczyszyn, Jedrzej Kaniewski, Jakub Mlokosiewicz, Anahita Rouyan, Tadeusz Satlawa, Sandeep Kumar, Victor Greiff, Konrad Krawczyk:
Machine-designed biotherapeutics: opportunities, feasibility and advantages of deep learning in computational antibody discovery. - Dong Lu, Rongrong Pan, Wenxuan Wu, Yanyan Zhang, Shensuo Li, Hong Xu, Jialan Huang, Jianhua Xia, Qun Wang, Xin Luan, Chao Lv, Weidong Zhang, Guofeng Meng:
FL-DTD: an integrated pipeline to predict the drug interacting targets by feedback loop-based network analysis. - Priya Gupta, Sureshkumar Venkadesan, Debasisa Mohanty:
Pf-Phospho: a machine learning-based phosphorylation sites prediction tool for Plasmodium proteins. - Jianmin Wang, Yanyi Chu, Jiashun Mao, Hyeon-Nae Jeon, Haiyan Jin, Amir Zeb, Yuil Jang, Kwang-Hwi Cho, Tao Song, Kyoung Tai No:
De novo molecular design with deep molecular generative models for PPI inhibitors. - Raphael Trevizani, Zhen Yan, Jason A. Greenbaum, Alessandro Sette, Morten Nielsen, Bjoern Peters:
A comprehensive analysis of the IEDB MHC class-I automated benchmark. - Mengyun Jiang, Weidong Ning, Shishi Wu, Xingwei Wang, Kun Zhu, Aomei Li, Yongyao Li, Shifeng Cheng, Bo Song:
Three-nucleotide periodicity of nucleotide diversity in a population enables the identification of open reading frames. - Mathew Karikomi, Peijie Zhou, Qing Nie:
DURIAN: an integrative deconvolution and imputation method for robust signaling analysis of single-cell transcriptomics data. - Lihong Peng, Feixiang Wang, Zhao Wang, Jingwei Tan, Li Huang, Xiongfei Tian, Guangyi Liu, Liqian Zhou:
Cell-cell communication inference and analysis in the tumour microenvironments from single-cell transcriptomics: data resources and computational strategies. - Wennan Chang, Chi Zhang, Sha Cao:
Response to 'Letter to the Editor: on the stability and internal consistency of component-wise sparse mixture regression based clustering', Zhang et al.. - Guiying Dong, Zi-Chao Zhang, Jianfeng Feng, Xing-Ming Zhao:
MorbidGCN: prediction of multimorbidity with a graph convolutional network based on integration of population phenotypes and disease network. - Yanglan Gan, Cheng Guo, Wenjing Guo, Guangwei Xu, Guobing Zou:
Entropy-based inference of transition states and cellular trajectory for single-cell transcriptomics. - Klaudia Adamowicz, Andreas Maier, Jan Baumbach, David B. Blumenthal:
Online in silico validation of disease and gene sets, clusterings or subnetworks with DIGEST. - Zhe Zhang, Miaomiao Zhu, Qi Xie, Robert M. Larkin, Xueping Shi, Bo Zheng:
CProtMEDIAS: clustering of amino acid sequences encoded by gene families by MErging and DIgitizing Aligned Sequences. - Hongzhun Wang, Feng Huang, Zhankun Xiong, Wen Zhang:
A heterogeneous network-based method with attentive meta-path extraction for predicting drug-target interactions. - Lang Zhou, Tingze Feng, Shuangbin Xu, Fangluan Gao, Tommy T. Lam, Qianwen Wang, Tianzhi Wu, Huina Huang, Li Zhan, Lin Li, Yi Guan, Zehan Dai, Guangchuang Yu:
ggmsa: a visual exploration tool for multiple sequence alignment and associated data. - Xianglin Zhang, Xiaodong Jia, Bixi Zhong, Lei Wei, Jiaqi Li, Wei Zhang, Huan Fang, Yanda Li, Yinying Lu, Xiaowo Wang:
Evaluating methylation of human ribosomal DNA at each CpG site reveals its utility for cancer detection using cell-free DNA. - Xinyi Xu, Xiaokang Yu, Gang Hu, Kui Wang, Jingxiao Zhang, Xiangjie Li:
Propensity score matching enables batch-effect-corrected imputation in single-cell RNA-seq analysis. - Wei Zhang, Qiaozhen Meng, Jianxin Wang, Fei Guo:
HDIContact: a novel predictor of residue-residue contacts on hetero-dimer interfaces via sequential information and transfer learning strategy. - Yunda Si, Chengfei Yan:
Protein complex structure prediction powered by multiple sequence alignments of interologs from multiple taxonomic ranks and AlphaFold2. - Tri Minh Nguyen, Thin Nguyen, Truyen Tran:
Mitigating cold-start problems in drug-target affinity prediction with interaction knowledge transferring. - Neeladri Sen, Ivan Anishchenko, Nicola Bordin, Ian Sillitoe, Sameer Velankar, David Baker, Christine A. Orengo:
Characterizing and explaining the impact of disease-associated mutations in proteins without known structures or structural homologs. - Xin Wang, Xia Cao, Yuantao Feng, Maozu Guo, Guoxian Yu, Jun Wang:
ELSSI: parallel SNP-SNP interactions detection by ensemble multi-type detectors. - Pâmela M. Rezende, Joicymara S. Xavier, David B. Ascher, Gabriel R. Fernandes, Douglas E. V. Pires:
Evaluating hierarchical machine learning approaches to classify biological databases. - Sirvan Khalighi, Peronne Joseph, Deepak Babu, Salendra Singh, Thomas LaFramboise, Kishore Guda, Vinay Varadan:
SYSMut: decoding the functional significance of rare somatic mutations in cancer. - Hui Yu, Shiyu Zhao, Jianyu Shi:
STNN-DDI: a Substructure-aware Tensor Neural Network to predict Drug-Drug Interactions. - Ho-Jin Gwak, Mina Rho:
ViBE: a hierarchical BERT model to identify eukaryotic viruses using metagenome sequencing data. - Kandasamy Saravanakumar, Sugavaneswaran Siva Santosh, Mohamedali Afaan Ahamed, Anbazhagan Sathiyaseelan, Ghazala Sultan, Navabshan Irfan, Davoodbasha Mubarak Ali, Myeong-Hyeon Wang:
Bioinformatics strategies for studying the molecular mechanisms of fungal extracellular vesicles with a focus on infection and immune responses. - Yunhee Jeong, Lisa Barros de Andrade e Sousa, Dominik Thalmeier, Reka Toth, Marlene Ganslmeier, Kersten Breuer, Christoph Plass, Pavlo Lutsik:
Systematic evaluation of cell-type deconvolution pipelines for sequencing-based bulk DNA methylomes. - Wenjuan Nie, Lei Deng:
TSNAPred: predicting type-specific nucleic acid binding residues via an ensemble approach. - Tiago Pereira, Maryam Abbasi, Rita I. Oliveira, Romina A. Guedes, Jorge A. R. Salvador, Joel P. Arrais:
Deep generative model for therapeutic targets using transcriptomic disease-associated data - USP7 case study. - Young-Jun Jeon, Md. Mehedi Hasan, Hyun Woo Park, Ki Wook Lee, Balachandran Manavalan:
TACOS: a novel approach for accurate prediction of cell-specific long noncoding RNAs subcellular localization. - Hiroyuki Kurata, Sho Tsukiyama, Balachandran Manavalan:
iACVP: markedly enhanced identification of anti-coronavirus peptides using a dataset-specific word2vec model. - Bing Wang, Yue Wang, Yu Chen, Mengmeng Gao, Jie Ren, Yueshuai Guo, Chenghao Situ, Yaling Qi, Hui Zhu, Yan Li, Xuejiang Guo:
DeepSCP: utilizing deep learning to boost single-cell proteome coverage. - Carlos H. M. Rodrigues, Douglas E. V. Pires, Tom L. Blundell, David B. Ascher:
Structural landscapes of PPI interfaces. - Varun S. Sharma, Andrea Fossati, Rodolfo Ciuffa, Marija Buljan, Evan G. Williams, Zhen Chen, Wenguang Shao, Patrick G. A. Pedrioli, Anthony W. Purcell, María Rodríguez Martínez, Jiangning Song, Matteo Manica, Ruedi Aebersold, Chen Li:
PCfun: a hybrid computational framework for systematic characterization of protein complex function.
Volume 23, Number 5, September 2022
- Minghao Yang, Zhi-An Huang, Wenhao Gu, Kun Han, Wenying Pan, Xiao Yang, Zexuan Zhu:
Prediction of biomarker-disease associations based on graph attention network and text representation. - Guanjin Qu, Zihui Yan, Huaming Wu:
Clover: tree structure-based efficient DNA clustering for DNA-based data storage. - Biqing Zhu, Hongyu Li, Le Zhang, Sreeganga S. Chandra, Hongyu Zhao:
A Markov random field model-based approach for differentially expressed gene detection from single-cell RNA-seq data. - Pei Chen, Jiayuan Zhong, Kun Yang, Xuhang Zhang, Yingqi Chen, Rui Liu:
TPD: a web tool for tipping-point detection based on dynamic network biomarker. - Jing Hu, Jie Gao, Xiaomin Fang, Zijing Liu, Fan Wang, Weili Huang, Hua Wu, Guodong Zhao:
DTSyn: a dual-transformer-based neural network to predict synergistic drug combinations. - Nick Keur, Isis Ricaño-Ponce, Vinod Kumar, Vasiliki Matzaraki:
A systematic review of analytical methods used in genetic association analysis of the X-chromosome. - Sini Junttila, Johannes Smolander, Laura L. Elo:
Benchmarking methods for detecting differential states between conditions from multi-subject single-cell RNA-seq data. - Li Peng, Cheng Yang, Li Huang, Xiang Chen, Xiangzheng Fu, Wei Liu:
RNMFLP: Predicting circRNA-disease associations based on robust nonnegative matrix factorization and label propagation. - Md. Sohrawordi, Md. Ali Hossain, Md. Al Mehedi Hasan:
PLP_FS: prediction of lysine phosphoglycerylation sites in protein using support vector machine and fusion of multiple F_Score feature selection. - Haiyue Wang, Xiaoke Ma:
Learning discriminative and structural samples for rare cell types with deep generative model. - Vishakha Gautam, Rahul Gupta, Deepti Gupta, Anubhav Ruhela, Aayushi Mittal, Sanjay Kumar Mohanty, Sakshi Arora, Ria Gupta, Chandan Saini, Debarka Sengupta, Natarajan Arul Murugan, Gaurav Ahuja:
deepGraphh: AI-driven web service for graph-based quantitative structure-activity relationship analysis. - Xudong Zhao, Tong Liu, Guohua Wang:
Ensemble classification based signature discovery for cancer diagnosis in RNA expression profiles across different platforms. - Xuwen Wang, Ying Xu, Ruoyu Liu, Xin Lai, Yuqian Liu, Shenjie Wang, Xuanping Zhang, Jiayin Wang:
PEcnv: accurate and efficient detection of copy number variations of various lengths. - Xilin Shen, Hongru Shen, Dan Wu, Mengyao Feng, Jiani Hu, Jilei Liu, Yichen Yang, Meng Yang, Yang Li, Lei Shi, Kexin Chen, Xiangchun Li:
Scalable batch-correction approach for integrating large-scale single-cell transcriptomes. - Hantao Shu, Fan Ding, Jingtian Zhou, Yexiang Xue, Dan Zhao, Jianyang Zeng, Jianzhu Ma:
Boosting single-cell gene regulatory network reconstruction via bulk-cell transcriptomic data. - Yun Zuo, Yue Hong, Xiangxiang Zeng, Qiang Zhang, Xiangrong Liu:
MLysPRED: graph-based multi-view clustering and multi-dimensional normal distribution resampling techniques to predict multiple lysine sites. - Abhishek Vijayan, Shadma Fatima, Arcot Sowmya, Fatemeh Vafaee:
Blood-based transcriptomic signature panel identification for cancer diagnosis: benchmarking of feature extraction methods. - Leroy Bondhus, Roshni Varma, Yenifer Hernandez, Valerie A. Arboleda:
Balancing the transcriptome: leveraging sample similarity to improve measures of gene specificity. - Zhengzheng Lou, Zhaoxu Cheng, Hui Li, Zhixia Teng, Yang Liu, Zhen Tian:
Predicting miRNA-disease associations via learning multimodal networks and fusing mixed neighborhood information. - Wei-Xin Hu, Yu Rong, Yan Guo, Feng Jiang, Wen Tian, Hao Chen, Shan-Shan Dong, Tie-Lin Yang:
ExsgRNA: reduce off-target efficiency by on-target mismatched sgRNA. - Hao Peng, Jiayuan Zhong, Pei Chen, Rui Liu:
Identifying the critical states of complex diseases by the dynamic change of multivariate distribution. - Fei Liu, Xiangkang Jiang, Jingyuan Yang, Jiawei Tao, Mao Zhang:
A chronotherapeutics-applicable multi-target therapeutics based on AI: Example of therapeutic hypothermia. - Vinícius de Almeida Paiva, Murillo Ventura Mendonça, Sabrina de Azevedo Silveira, David B. Ascher, Douglas E. V. Pires, Sandro C. Izidoro:
GASS-Metal: identifying metal-binding sites on protein structures using genetic algorithms. - Jiayu Shang, Yanni Sun:
CHERRY: a Computational metHod for accuratE pRediction of virus-pRokarYotic interactions using a graph encoder-decoder model. - Qunzhuo Wu, Zhaohong Deng, Xiaoyong Pan, Hong-Bin Shen, Kup-Sze Choi, Shitong Wang, Jing Wu, Dong-Jun Yu:
MDGF-MCEC: a multi-view dual attention embedding model with cooperative ensemble learning for CircRNA-disease association prediction. - Neelam Sharma, Naorem Leimarembi Devi, Shipra Jain, Gajendra P. S. Raghava:
ToxinPred2: an improved method for predicting toxicity of proteins. - Tanmaya Kumar Sahu, Prabina Kumar Meher, Nalini Kanta Choudhury, Atmakuri Ramakrishna Rao:
A comparative analysis of amino acid encoding schemes for the prediction of flexible length linear B-cell epitopes. - Weijie Zhang, Pengyun Gong, Yichu Shan, Lili Zhao, Hongke Hu, Qiushi Wei, Zhen Liang, Chao Liu, Lihua Zhang, Yukui Zhang:
SpotLink enables sensitive and precise identification of site nonspecific cross-links at the proteome scale. - Shouzhi Chen, Qing Li, Jianping Zhao, Yannan Bin, Chunhou Zheng:
NeuroPred-CLQ: incorporating deep temporal convolutional networks and multi-head attention mechanism to predict neuropeptides. - Hongyu Ding, Junwei Luo:
MAMnet: detecting and genotyping deletions and insertions based on long reads and a deep learning approach. - Gang Xu, Qinghua Wang, Jianpeng Ma:
Corrigendum to 'OPUS-Rota4: a gradient-based protein side-chain modeling framework assisted by deep learning-based predictors'. - Kai Zheng, Haochen Zhao, Qichang Zhao, Bin Wang, Xin Gao, Jianxin Wang:
NASMDR: a framework for miRNA-drug resistance prediction using efficient neural architecture search and graph isomorphism networks. - Rui Yin, Xianghe Zhu, Min Zeng, Pengfei Wu, Min Li, Chee Keong Kwoh:
A framework for predicting variable-length epitopes of human-adapted viruses using machine learning methods. - Tanmaya Kumar Sahu, Amit Kumar Singh, Shikha Mittal, Shailendra Kumar Jha, Sundeep Kumar, Sherry Rachel Jacob, Kuldeep Singh:
G-DIRT: a web server for identification and removal of duplicate germplasms based on identity-by-state analysis using single nucleotide polymorphism genotyping data. - Haojie Huang, Gongming Zhou, Xuejun Liu, Lei Deng, Chen Wu, Dachuan Zhang, Hui Liu:
Contrastive learning-based computational histopathology predict differential expression of cancer driver genes. - Congxue Hu, Yingqi Xu, Feng Li, Wanqi Mi, He Yu, Xinran Wang, Xin Wen, Shuaijun Chen, Xia Li, Yanjun Xu, Yunpeng Zhang:
Identifying and characterizing drug sensitivity-related lncRNA-TF-gene regulatory triplets. - Tianjiao Zhang, Yuran Jia, Hongfei Li, Dali Xu, Jie Zhou, Guohua Wang:
CRISPRCasStack: a stacking strategy-based ensemble learning framework for accurate identification of Cas proteins. - Lei Wang, Leon Wong, Zheng-Wei Li, Yuan Huang, Xiao-Rui Su, Bo-Wei Zhao, Zhuhong You:
A machine learning framework based on multi-source feature fusion for circRNA-disease association prediction. - Siyuan Liu, Yusong Wang, Yifan Deng, Liang He, Bin Shao, Jian Yin, Nanning Zheng, Tie-Yan Liu, Tong Wang:
Improved drug-target interaction prediction with intermolecular graph transformer. - Chenyang Zhang, Minjie Mou, Ying Zhou, Wei Zhang, Xichen Lian, Shuiyang Shi, Mingkun Lu, Huaicheng Sun, Fengcheng Li, Yunxia Wang, Zhenyu Zeng, Zhaorong Li, Bing Zhang, Yunqing Qiu, Feng Zhu, Jianqing Gao:
Biological activities of drug inactive ingredients. - Julius O. B. Jacobsen, Catherine Kelly, Valentina Cipriani, Peter N. Robinson, Damian Smedley:
Evaluation of phenotype-driven gene prioritization methods for Mendelian diseases. - Jiping Wang, Hongmin Liang, Qingzhao Zhang, Shuangge Ma:
Replicability in cancer omics data analysis: measures and empirical explorations. - Huihui Yan, Yuanyuan Xie, Yao Liu, Leer Yuan, Rong Sheng:
ComABAN: refining molecular representation with the graph attention mechanism to accelerate drug discovery. - Jiye Wang, Chaofeng Lou, Guixia Liu, Weihua Li, Zengrui Wu, Yun Tang:
Profiling prediction of nuclear receptor modulators with multi-task deep learning methods: toward the virtual screening. - Mingyuan Ma, Sen Na, Xiaolu Zhang, Congzhou Chen, Jin Xu:
SFGAE: a self-feature-based graph autoencoder model for miRNA-disease associations prediction. - Wengang Wang, Hailin Chen:
Predicting miRNA-disease associations based on graph attention networks and dual Laplacian regularized least squares. - Yuansong Zeng, Zhuoyi Wei, Fengqi Zhong, Zixiang Pan, Yutong Lu, Yuedong Yang:
A parameter-free deep embedded clustering method for single-cell RNA-seq data. - Wei Liu, Xingen Sun, Li Yang, Kaiwen Li, Yu Yang, Xiangzheng Fu:
NSCGRN: a network structure control method for gene regulatory network inference. - Zhao-Yue Zhang, Lin Ning, Xiucai Ye, Yu-He Yang, Yasunori Futamura, Tetsuya Sakurai, Hao Lin:
iLoc-miRNA: extracellular/intracellular miRNA prediction using deep BiLSTM with attention mechanism. - Tao Zeng, Bernard Andes Hess Jr., Fan Zhang, Ruibo Wu:
Bio-inspired chemical space exploration of terpenoids. - Wei Li, Han Zhang, Minghe Li, Mingjing Han, Yanbin Yin:
MGEGFP: a multi-view graph embedding method for gene function prediction based on adaptive estimation with GCN. - Asli Boyraz, Vera Pawlowsky-Glahn, Juan José Egozcue, Aybar Can Acar:
Principal microbial groups: compositional alternative to phylogenetic grouping of microbiome data. - Alex G. C. de Sá, Yangyang Long, Stephanie Portelli, Douglas E. V. Pires, David B. Ascher:
toxCSM: comprehensive prediction of small molecule toxicity profiles. - Bin Liu, Dimitrios Papadopoulos, Fragkiskos D. Malliaros, Grigorios Tsoumakas, Apostolos N. Papadopoulos:
Multiple similarity drug-target interaction prediction with random walks and matrix factorization. - Tianjiao Zhang, Jie Zhou, Wentao Gao, Yuran Jia, Yanan Wei, Guohua Wang:
Complex genome assembly based on long-read sequencing. - Annika L. Gable, Damian Szklarczyk, David Lyon, João F. Matias Rodrigues, Christian von Mering:
Systematic assessment of pathway databases, based on a diverse collection of user-submitted experiments. - Jian Liu, Yichen Pan, Zhihan Ruan, Jun Guo:
SCDD: a novel single-cell RNA-seq imputation method with diffusion and denoising. - Quan Xu, Yueyue Liu, Jifang Hu, Xiaohong Duan, Niuben Song, Jiale Zhou, Jincheng Zhai, Junyan Su, Siyao Liu, Fan Chen, Wei Zheng, Zhongjia Guo, Hexiang Li, Qiming Zhou, Beifang Niu:
OncoPubMiner: a platform for mining oncology publications. - Patryk Jarnot, Joanna Ziemska-Legiecka, Marcin Grynberg, Aleksandra Gruca:
Insights from analyses of low complexity regions with canonical methods for protein sequence comparison. - Lijuan Yang, Guanghui Yang, Zhi-Tong Bing, Yuan Tian, Liang Huang, Yuzhen Niu, Lei Yang:
Accelerating the discovery of anticancer peptides targeting lung and breast cancers with the Wasserstein autoencoder model and PSO algorithm. - Laura Zaragoza-Infante, Valentin Junet, Nikos Pechlivanis, Styliani-Christina Fragkouli, Serovpe Amprachamian, Triantafyllia Koletsa, Anastasia Chatzidimitriou, Maria Papaioannou, Kostas Stamatopoulos, Andreas Agathangelidis, Fotis E. Psomopoulos:
IgIDivA: immunoglobulin intraclonal diversification analysis. - Yaqi Zhang, Gancheng Zhu, Kewei Li, Fei Li, Lan Huang, Meiyu Duan, Fengfeng Zhou:
HLAB: learning the BiLSTM features from the ProtBert-encoded proteins for the class I HLA-peptide binding prediction. - Lu Liang, Ye Liu, Bo Kang, Ru Wang, Meng-Yu Sun, Qi Wu, Xiangfei Meng, Jianping Lin:
Large-scale comparison of machine learning algorithms for target prediction of natural products. - Qiao Ning, Jinmou Li:
DLF-Sul: a multi-module deep learning framework for prediction of S-sulfinylation sites in proteins. - Han Sun, Xiaoyun Huang, Ban Huo, Yuting Tan, Tingting He, Xingpeng Jiang:
Detecting sparse microbial association signals adaptively from longitudinal microbiome data based on generalized estimating equations. - Anjali Dhall, Sumeet Patiyal, Gajendra P. S. Raghava:
HLAncPred: a method for predicting promiscuous non-classical HLA binding sites. - Rong Gao, Jinling Yan, Peiluan Li, Luonan Chen:
Detecting the critical states during disease development based on temporal network flow entropy. - Fabrício Martins Lopes, Matheus Henrique Pimenta-Zanon:
Letter on the results of the BASiNET method in the paper 'A systematic evaluation of computational tools for lncRNA identification'. - Sanjay Kumar, Geethu S. Kumar, Subhrangsu Sundar Maitra, Petr Malý, Shiv Bharadwaj, Pradeep Sharma, Vivek Dhar Dwivedi:
Viral informatics: bioinformatics-based solution for managing viral infections. - Ping Xuan, Shuai Wang, Hui Cui, Yue Zhao, Tiangang Zhang, Peiliang Wu:
Learning global dependencies and multi-semantics within heterogeneous graph for predicting disease-related lncRNAs. - Wenkai Han, Yuqi Cheng, Jiayang Chen, Huawen Zhong, Zhihang Hu, Siyuan Chen, Licheng Zong, Liang Hong, Ting-Fung Chan, Irwin King, Xin Gao, Yu Li:
Self-supervised contrastive learning for integrative single cell RNA-seq data analysis. - Ji Lv, Guixia Liu, Junli Hao, Yuan Ju, Binwen Sun, Ying Sun:
Computational models, databases and tools for antibiotic combinations. - Giovanna Carpi, Lev Gorenstein, Timothy T. Harkins, Mehrzad Samadi, Pankaj Vats:
A GPU-accelerated compute framework for pathogen genomic variant identification to aid genomic epidemiology of infectious disease: a malaria case study. - Sumeet Patiyal, Anjali Dhall, Gajendra P. S. Raghava:
A deep learning-based method for the prediction of DNA interacting residues in a protein. - Jiani Ma, Hui Liu, Yumeng Mao, Lin Zhang:
LRTCLS: low-rank tensor completion with Laplacian smoothing regularization for unveiling the post-transcriptional machinery of N6-methylation (m6A)-mediated diseases. - Katarzyna Sidorczuk, Przemyslaw Gagat, Filip Pietluch, Jakub Kala, Dominik Rafacz, Laura Bakala, Jadwiga Slowik, Rafal Kolenda, Stefan Rödiger, Legana C. H. W. Fingerhut, Ira Cooke, Pawel Mackiewicz, Michal Burdukiewicz:
Benchmarks in antimicrobial peptide prediction are biased due to the selection of negative data. - Xiaotao Shen, Wei Shao, Chuchu Wang, Liang Liang, Songjie Chen, Sai Zhang, Mirabela Rusu, Michael P. Snyder:
Deep learning-based pseudo-mass spectrometry imaging analysis for precision medicine. - Hui Liu, Yibiao Huang, Xuejun Liu, Lei Deng:
Attention-wise masked graph contrastive learning for predicting molecular property. - Stephen Bonner, Ufuk Kirik, Ola Engkvist, Jian Tang, Ian P. Barrett:
Implications of topological imbalance for representation learning on biomedical knowledge graphs. - Renchu Guan, Haoyu Pang, Yanchun Liang, Zhongjun Shao, Xin Gao, Dong Xu, Xiaoyue Feng:
Discovering trends and hotspots of biosafety and biosecurity research via machine learning. - Paula J. Gómez-González, Susana G. Campino, Jody Phelan, Taane G. Clark:
Portable sequencing of Mycobacterium tuberculosis for clinical and epidemiological applications. - Yuansong Zeng, Zhuoyi Wei, Weijiang Yu, Rui Yin, Yuchen Yuan, Bingling Li, Zhonghui Tang, Yutong Lu, Yuedong Yang:
Spatial transcriptomics prediction from histology jointly through Transformer and graph neural networks. - Yujie Wang, Gang Zhou, Tianhao Guan, Yan Wang, Chenxu Xuan, Tao Ding, Jie Gao:
A network-based matrix factorization framework for ceRNA co-modules recognition of cancer genomic data. - Fan Yang, Shuaijie Zhang, Wei Pan, Ruiyuan Yao, Weiguo Zhang, Yanchun Zhang, Guoyin Wang, Qianghua Zhang, Yunlong Cheng, Jihua Dong, Chunyang Ruan, Lizhen Cui, Hao Wu, Fuzhong Xue:
Signaling repurposable drug combinations against COVID-19 by developing the heterogeneous deep herb-graph method. - Gang Xu, Yilin Wang, Qinghua Wang, Jianpeng Ma:
Studying protein-protein interaction through side-chain modeling method OPUS-Mut. - Natàlia Segura-Alabart, Francesc Serratosa, Sergio Gómez, Alberto Fernández:
Nonunique UPGMA clusterings of microsatellite markers. - Bernardina Scafuri, Anna Verdino, Nancy D'Arminio, Anna Marabotti:
Computational methods to assist in the discovery of pharmacological chaperones for rare diseases. - Yongsan Yang, Fengcui Qian, Xuecang Li, Yanyu Li, Liwei Zhou, Qiuyu Wang, Xinyuan Zhou, Jian Zhang, Chao Song, Zhengmin Yu, Ting Cui, Chenchen Feng, Jiang Zhu, Desi Shang, Jiaqi Liu, Mengfei Sun, Yuexin Zhang, Huifang Tang, Chunquan Li:
GREAP: a comprehensive enrichment analysis software for human genomic regions. - Tianjian Liang, Chen Jiang, Jiayi Yuan, Yasmin Othman, Xiang-Qun Xie, Zhiwei Feng:
Differential performance of RoseTTAFold in antibody modeling. - Xiao Yuan, Peng Zhang:
Revisiting benchmark study for response to methodological critiques of 'Evaluation of phenotype-driven gene prioritization methods for Mendelian diseases'. - Jana Schor, Patrick Scheibe, Matthias Bernt, Wibke Busch, Chih Lai, Jörg Hackermüller:
AI for predicting chemical-effect associations at the chemical universe level - deepFPlearn. - Sujie Zhu, Weikaixin Kong, Jie Zhu, Liting Huang, Shixin Wang, Suzhen Bi, Zhengwei Xie:
The genetic algorithm-aided three-stage ensemble learning method identified a robust survival risk score in patients with glioma. - Li Huang, Li Zhang, Xing Chen:
Updated review of advances in microRNAs and complex diseases: taxonomy, trends and challenges of computational models. - Jing J. Liang, Zong-Wei Li, Cai-Tong Yue, Zhuo Hu, Han Cheng, Zexian Liu, Weifeng Guo:
Multi-modal optimization to identify personalized biomarkers for disease prediction of individual patients with cancer. - Jiawei Zou, Fulan Deng, Miaochen Wang, Zhen Zhang, Zheqi Liu, Xiaobin Zhang, Rong Hua, Ke Chen, Xin Zou, Jie Hao:
scCODE: an R package for data-specific differentially expressed gene detection on single-cell RNA-sequencing data. - Sandeep Kumar Dhanda, Jitendra Malviya, Sudheer Gupta:
Not all T cell epitopes are equally desired: a review of in silico tools for the prediction of cytokine-inducing potential of T-cell epitopes. - Pedro Blecua, Veronica Davalos, Izar de Villasante, Angelika Merkel, Eva Musulen, Laia Coll-Sanmartin, Manel Esteller:
Refinement of computational identification of somatic copy number alterations using DNA methylation microarrays illustrated in cancers of unknown primary. - Junhao Su, Zhenxian Zheng, Syed Shakeel Ahmed, Tak Wah Lam, Ruibang Luo:
Clair3-trio: high-performance Nanopore long-read variant calling in family trios with trio-to-trio deep neural networks. - Qian Ding, Wenyi Yang, Meng Luo, Chang Xu, Zhaochun Xu, Fenglan Pang, Yideng Cai, Anastasia A. Anashkina, Xi Su, Na Chen, Qinghua Jiang:
CBLRR: a cauchy-based bounded constraint low-rank representation method to cluster single-cell RNA-seq data. - Daniel Toro-Domínguez, Jordi Martorell-Marugan, Manuel Martínez-Bueno, Raúl López-Domínguez, Elena Carnero-Montoro, Guillermo Barturen, Daniel Goldman, Michelle Petri, Pedro Carmona-Saez, Marta E. Alarcón-Riquelme:
Scoring personalized molecular portraits identify Systemic Lupus Erythematosus subtypes and predict individualized drug responses, symptomatology and disease progression. - Xinghu Qin, Charleston W. K. Chiang, Oscar E. Gaggiotti:
Deciphering signatures of natural selection via deep learning. - Daniele Mercatelli, Chiara Cabrelle, Pierangelo Veltri, Federico M. Giorgi, Pietro H. Guzzi:
Detection of pan-cancer surface protein biomarkers via a network-based approach on transcriptomics data. - Giuseppe Agapito, Chiara Pastrello, Yun Niu, Igor Jurisica:
Pathway integration and annotation: building a puzzle with non-matching pieces and no reference picture. - Rui Zhang, Xumin Ni, Kai Yuan, Yuwen Pan, Shuhua Xu:
MultiWaverX: modeling latent sex-biased admixture history. - Ofir Yaish, Maor Asif, Yaron Orenstein:
A systematic evaluation of data processing and problem formulation of CRISPR off-target site prediction. - Guanhua Zou, Yilong Lin, Tianyang Han, Le Ou-Yang:
DEMOC: a deep embedded multi-omics learning approach for clustering single-cell CITE-seq data. - Hansi Zheng, Xiaoman Li, Haiyan Hu:
Letter to the editor: evaluating computational tools for lncRNA identification on independent datasets. - Shuo Shi, Qi Wang, Yunfei Shang, Congfan Bu, Mingming Lu, Meiye Jiang, Hao Zhang, Shuhuan Yu, Jingyao Zeng, Zaichao Zhang, Zhenglin Du, Jing-Fa Xiao:
TSomVar: a tumor-only somatic and germline variant identification method with random forest. - Gui-Yan Xie, Chun-Jie Liu, An-Yuan Guo:
EVAtool: an optimized reads assignment tool for small ncRNA quantification and its application in extracellular vesicle datasets. - Lingling Zhao, Huiting Sun, Xinyi Cao, Naifeng Wen, Junjie Wang, Chunyu Wang:
Learning representations for gene ontology terms by jointly encoding graph structure and textual node descriptors. - Xuhua Yan, Ruiqing Zheng, Min Li:
GLOBE: a contrastive learning-based framework for integrating single-cell transcriptome datasets. - Mingyan Fang, Zheng Su, Hassan Abolhassani, Yuval Itan, Xin Jin, Lennart Hammarström:
VIPPID: a gene-specific single nucleotide variant pathogenicity prediction tool for primary immunodeficiency diseases. - Ling Luo, Po-Ting Lai, Chih-Hsuan Wei, Cecilia N. Arighi, Zhiyong Lu:
BioRED: a rich biomedical relation extraction dataset. - Qiaoming Liu, Ximei Luo, Jie Li, Guohua Wang:
scESI: evolutionary sparse imputation for single-cell transcriptomes from nearest neighbor cells. - Zhi Ma, Yang Young Lu, Yiwen Wang, Renhao Lin, Zizi Yang, Fang Zhang, Ying Wang:
Metric learning for comparing genomic data with triplet network. - Jingsi Ming, Zhixiang Lin, Jia Zhao, Xiang Wan, Tabula Microcebus Consortium, Can Yang, Angela Ruohao Wu:
FIRM: Flexible integration of single-cell RNA-sequencing data for large-scale multi-tissue cell atlas datasets. - Sreya Vadapalli, Habiba Abdelhalim, Saman Zeeshan, Zeeshan Ahmed:
Artificial intelligence and machine learning approaches using gene expression and variant data for personalized medicine. - Yi Cao, Zhen-Qun Yang, Xu-Lu Zhang, Wenqi Fan, Yaowei Wang, Jiajun Shen, Dong-Qing Wei, Qing Li, Xiao-Yong Wei:
Identifying the kind behind SMILES - anatomical therapeutic chemical classification using structure-only representations. - Zhe Wang, Hong Pan, Huiyong Sun, Yu Kang, Huanxiang Liu, Dongsheng Cao, Tingjun Hou:
fastDRH: a webserver to predict and analyze protein-ligand complexes based on molecular docking and MM/PB(GB)SA computation. - Shaokai Wang, Haochen Zhao:
SADeepcry: a deep learning framework for protein crystallization propensity prediction using self-attention and auto-encoder networks. - Chien Lee, Bo-Han Su, Yufeng Jane Tseng:
Comparative studies of AlphaFold, RoseTTAFold and Modeller: a case study involving the use of G-protein-coupled receptors. - Qiang Tang, Fulei Nie, Qi Zhao, Wei Chen:
A merged molecular representation deep learning method for blood-brain barrier permeability prediction. - Feng Zhang, Chen Yang, Yihao Wang, Huiyuan Jiao, Zhiming Wang, Jianfeng Shen, Lingjie Li:
FitDevo: accurate inference of single-cell developmental potential using sample-specific gene weight. - Hongyan Shi, Shengli Zhang, Xinjie Li:
R5hmCFDV: computational identification of RNA 5-hydroxymethylcytosine based on deep feature fusion and deep voting. - Chanwoo Park, Boram Kim, Taesung Park:
DeepHisCoM: deep learning pathway analysis using hierarchical structural component models. - Siyu Hou, Peng Zhang, Kuo Yang, Lan Wang, Changzheng Ma, Yanda Li, Shao Li:
Decoding multilevel relationships with the human tissue-cell-molecule network. - Ping Xuan, Xiaowen Zhang, Yu Zhang, Kaimiao Hu, Toshiya Nakaguchi, Tiangang Zhang:
multi-type neighbors enhanced global topology and pairwise attribute learning for drug-protein interaction prediction. - Xudong Zhang, Gan Wang, Xiangyu Meng, Shuang Wang, Ying Zhang, Alfonso Rodríguez-Patón, Jianmin Wang, Xun Wang:
Molormer: a lightweight self-attention-based method focused on spatial structure of molecular graph for drug-drug interactions prediction. - Kang Jin, Daniel J. Schnell, Guangyuan Li, Nathan Salomonis, V. B. Surya Prasath, Rhonda Szczesniak, Bruce J. Aronow:
CellDrift: inferring perturbation responses in temporally sampled single-cell data. - Lu-Xiang Guo, Zhu-Hong You, Lei Wang, Changqing Yu, Bo-Wei Zhao, Zhong-Hao Ren, Jie Pan:
A novel circRNA-miRNA association prediction model based on structural deep neural network embedding. - Hao Liu, Jiaqi Dai, Ke Li, Yang Sun, Haoran Wei, Hong Wang, Chunxia Zhao, Dao Wen Wang:
Performance evaluation of computational methods for splice-disrupting variants and improving the performance using the machine learning-based framework. - Qingjian Chen, Qi-Nian Wu, Yu-Ming Rong, Shixiang Wang, Zhixiang Zuo, Long Bai, Bei Zhang, Shuqiang Yuan, Qi Zhao:
Deciphering clonal dynamics and metastatic routines in a rare patient of synchronous triple-primary tumors and multiple metastases with MPTevol. - Suruchi Aggarwal, Anurag Raj, Dhirendra Kumar, Debasis Dash, Amit Kumar Yadav:
False discovery rate: the Achilles' heel of proteogenomics. - Qiguo Dai, Ziqiang Liu, Zhaowei Wang, Xiaodong Duan, Maozu Guo:
GraphCDA: a hybrid graph representation learning framework based on GCN and GAT for predicting disease-associated circRNAs. - Xiangmei Li, Yalan He, Jiashuo Wu, Jiayue Qiu, Ji Li, Qian Wang, Ying Jiang, Junwei Han:
A novel pathway mutation perturbation score predicts the clinical outcomes of immunotherapy. - Xiaobin Wu, Yuan Zhou:
GE-Impute: graph embedding-based imputation for single-cell RNA-seq data. - Zhong-Hao Ren, Zhu-Hong You, Changqing Yu, Li-Ping Li, Yongjian Guan, Lu-Xiang Guo, Jie Pan:
A biomedical knowledge graph-based method for drug-drug interactions prediction through combining local and global features with deep neural networks.
Volume 23, Number 6, November 2022
- Ricky Wai Tak Leung, Xiaosen Jiang, Xueqing Zong, Yanhong Zhang, Xinlin Hu, Yaohua Hu, Jing Qin:
CORN - Condition Orientated Regulatory Networks: bridging conditions to gene networks. - Zhen Tian, Xiangyu Peng, Haichuan Fang, Wenjie Zhang, Qiguo Dai, Yangdong Ye:
MHADTI: predicting drug-target interactions via multiview heterogeneous information network embedding with hierarchical attention mechanisms. - Timothy Warwick, Sandra Seredinski, Nina M. Krause, Jasleen Kaur Bains, Lara Althaus, James A Oo, Alessandro Bonetti, Anne Dueck, Stefan Engelhardt, Harald Schwalbe, Matthias S. Leisegang, Marcel H. Schulz, Ralf P. Brandes:
A universal model of RNA.DNA: DNA triplex formation accurately predicts genome-wide RNA-DNA interactions. - Yuqing Qian, Yijie Ding, Quan Zou, Fei Guo:
Identification of drug-side effect association via restricted Boltzmann machines with penalized term. - Julio Vera, Xin Lai, Andreas Baur, Michael Erdmann, Shailendra K. Gupta, Cristiano Guttà, Lucie Heinzerling, Markus V. Heppt, Philipp Maximilian Kazmierczak, Manfred Kunz, Christopher Lischer, Brigitte M. Pützer, Markus Rehm, Christian Ostalecki, Jimmy Retzlaff, Stephan Witt, Olaf Wolkenhauer, Carola Berking:
Melanoma 2.0. Skin cancer as a paradigm for emerging diagnostic technologies, computational modelling and artificial intelligence. - Xing Chen, Li Huang:
Computational model for ncRNA research. - Jie Wang, Zihao Shen, Yichen Liao, Zhen Yuan, Shiliang Li, Gaoqi He, Man Lan, Xuhong Qian, Kai Zhang, Honglin Li:
Multi-modal chemical information reconstruction from images and texts for exploring the near-drug space. - Dong Ouyang, Yong Liang, Jianjun Wang, Xiaoying Liu, Shengli Xie, Rui Miao, Ning Ai, Le Li, Qi Dang:
Predicting multiple types of miRNA-disease associations using adaptive weighted nonnegative tensor factorization with self-paced learning and hypergraph regularization. - Yuyao Huang, Jiesi Luo, Runyu Jing, Menglong Li:
Multi-model predictive analysis of RNA solvent accessibility based on modified residual attention mechanism. - Yixuan Qiao, Lianhe Zhao, Chunlong Luo, Yufan Luo, Yang Wu, Shengtong Li, Dechao Bu, Yi Zhao:
Multi-modality artificial intelligence in digital pathology. - Anna Christine Haber, Ulrich Sax, Fabian Prasser:
Open tools for quantitative anonymization of tabular phenotype data: literature review. - Bao-Min Liu, Ying-Lian Gao, Dai-Jun Zhang, Feng Zhou, Juan Wang, Chun-Hou Zheng, Jin-Xing Liu:
A new framework for drug-disease association prediction combing light-gated message passing neural network and gated fusion mechanism. - Liang Cheng:
Omics data analysis and integration for COVID-19 patients - editorial. - Xiu-Ju George Zhao, Hui Cao:
Linking research of biomedical datasets. - Menglan Cai, Anna Vesely, Xu Chen, Limin Li, Jelle J. Goeman:
NetTDP: permutation-based true discovery proportions for differential co-expression network analysis. - Hongwei Tu, Yanqiang Han, Zhilong Wang, Jinjin Li:
Clustered tree regression to learn protein energy change with mutated amino acid. - Correction: Insights from analyses of low complexity regions with canonical methods for protein sequence comparison.
- Kai Zheng, Xin-Lu Zhang, Lei Wang, Zhu-Hong You, Zhaohui Zhan, Hao-Yuan Li:
Line graph attention networks for predicting disease-associated Piwi-interacting RNAs. - Visanu Wanchai, Piroon Jenjaroenpun, Thongpan Leangapichart, Gerard Arrey, Charles M. Burnham, Maria C. Tümmler, Jesus Delgado-Calle, Birgitte Regenberg, Intawat Nookaew:
CReSIL: accurate identification of extrachromosomal circular DNA from long-read sequences. - Xudong Zhao, Jingwen Zhai, Tong Liu, Guohua Wang:
Ensemble classification based feature selection: a case of identification on plant pentatricopeptide repeat proteins. - Ebony Rose Watson, Ariane Mora, Atefeh Taherian Fard, Jessica Cara Mar:
How does the structure of data impact cell-cell similarity? Evaluating how structural properties influence the performance of proximity metrics in single cell RNA-seq data. - Trishala Das, Harbinder Kaur, Pratibha Gour, Kartikay Prasad, Andrew M. Lynn, Amresh Prakash, Vijay Kumar:
Intersection of network medicine and machine learning towards investigating the key biomarkers and pathways underlying amyotrophic lateral sclerosis: a systematic review. - Duy Pham, Buu Truong, Khai Tran, Guiyan Ni, Dat Nguyen, Trang T. H. Tran, Mai H. Tran, Duong Thuy Nguyen, Nam S. Vo, Quan Nguyen:
Assessing polygenic risk score models for applications in populations with under-represented genomics data: an example of Vietnam. - Li Zhang, Chun-Chun Wang, Xing Chen:
Predicting drug-target binding affinity through molecule representation block based on multi-head attention and skip connection. - Hao Zhang, Nan Zhang, Wantao Wu, Ran Zhou, Shuyu Li, Zeyu Wang, Ziyu Dai, Liyang Zhang, Zaoqu Liu, Jian Zhang, Peng Luo, Zhixiong Liu, Quan Cheng:
Machine learning-based tumor-infiltrating immune cell-associated lncRNAs for predicting prognosis and immunotherapy response in patients with glioblastoma. - Simon Zhongyuan Tian, Guoliang Li, Duo Ning, Kai Jing, Yewen Xu, Yang Yang, Melissa Jane Fullwood, Pengfei Yin, Guangyu Huang, Dariusz Plewczynski, Jixian Zhai, Ziwei Dai, Wei Chen, Meizhen Zheng:
MCIBox: a toolkit for single-molecule multi-way chromatin interaction visualization and micro-domains identification. - Shidi Miao, Haobo Jia, Ke Cheng, Xiaohui Hu, Jing Li, Wenjuan Huang, Ruitao Wang:
Deep learning radiomics under multimodality explore association between muscle/fat and metastasis and survival in breast cancer patients. - Ziyan Feng, Zihao Shen, Honglin Li, Shiliang Li:
e-TSN: an interactive visual exploration platform for target-disease knowledge mapping from literature. - Li Huang, Li Zhang, Xing Chen:
Updated review of advances in microRNAs and complex diseases: experimental results, databases, webservers and data fusion. - Yue Bi, Fuyi Li, Xudong Guo, Zhikang Wang, Tong Pan, Yuming Guo, Geoffrey I. Webb, Jianhua Yao, Cangzhi Jia, Jiangning Song:
Clarion is a multi-label problem transformation method for identifying mRNA subcellular localizations. - Yansen Su, Minglu Wang, Pengpeng Wang, Chunhou Zheng, Yuansheng Liu, Xiangxiang Zeng:
Deep learning joint models for extracting entities and relations in biomedical: a survey and comparison. - César R. García-Jacas, Luis A. García-González, Felix Martinez-Rios, Issac P. Tapia-Contreras, Carlos A. Brizuela:
Handcrafted versus non-handcrafted (self-supervised) features for the classification of antimicrobial peptides: complementary or redundant? - Yanting Zhang, Hisanori Kiryu:
MODEC: an unsupervised clustering method integrating omics data for identifying cancer subtypes. - Hanyu Zhang, Yunxia Wang, Ziqi Pan, Xiuna Sun, Minjie Mou, Bing Zhang, Zhaorong Li, Honglin Li, Feng Zhu:
ncRNAInter: a novel strategy based on graph neural network to discover interactions between lncRNA and miRNA. - Qianmu Yuan, Sheng Chen, Yu Wang, Huiying Zhao, Yuedong Yang:
Alignment-free metal ion-binding site prediction from protein sequence through pretrained language model and multi-task learning. - Atlas M. Sardoo, Shaoqiang Zhang, Thomas N. Ferraro, Thomas M. Keck, Yong Chen:
Decoding brain memory formation by single-cell RNA sequencing. - Wenkai Yan, Zutan Li, Cong Pian, Yufeng Wu:
PlantBind: an attention-based multi-label neural network for predicting plant transcription factor binding sites. - Yuan Zhang, Qiaoyan Jiang, Ling Li, Zutan Li, Zhihui Xu, Yuanyuan Chen, Yang Sun, Cheng Liu, Zhengsheng Mao, Feng Chen, Hualan Li, Yue Cao, Cong Pian:
Predicting the structure of unexplored novel fentanyl analogues by deep learning model. - Binsheng He, Kun Wang, Ju Xiang, Pingping Bing, Min Tang, Geng Tian, Cheng Guo, Miao Xu, Jialiang Yang:
DGHNE: network enhancement-based method in identifying disease-causing genes through a heterogeneous biomedical network. - Yuhua Yao, Yaping Lv, Ling Tong, Yuebin Liang, Shuxue Xi, Binbin Ji, Guanglu Zhang, Ling Li, Geng Tian, Min Tang, Xiyue Hu, Shijun Li, Jialiang Yang:
ICSDA: a multi-modal deep learning model to predict breast cancer recurrence and metastasis risk by integrating pathological, clinical and gene expression data. - Qingxia Yang, Bo Li, Panpan Wang, Jicheng Xie, Yuhao Feng, Ziqiang Liu, Feng Zhu:
LargeMetabo: an out-of-the-box tool for processing and analyzing large-scale metabolomic data. - Bodhayan Prasad, Anthony J. Bjourson, Priyank Shukla:
Data-driven patient stratification of UK Biobank cohort suggests five endotypes of multimorbidity. - Elias Orouji, Ayush T. Raman:
Computational methods to explore chromatin state dynamics. - Stephen Bonner, Ian P. Barrett, Cheng Ye, Rowan Swiers, Ola Engkvist, Andreas Bender, Charles Tapley Hoyt, William L. Hamilton:
A review of biomedical datasets relating to drug discovery: a knowledge graph perspective. - Jitong Feng, Shengbo Wu, Hongpeng Yang, Chengwei Ai, Jianjun Qiao, Junhai Xu, Fei Guo:
Microbe-bridged disease-metabolite associations identification by heterogeneous graph fusion. - Jiahui Ji, Maryam Anwar, Enrico Petretto, Costanza Emanueli, Prashant Kumar Srivastava:
PPMS: A framework to Profile Primary MicroRNAs from Single-cell RNA-sequencing datasets. - Yang Li, Xue-Gang Hu, Lei Wang, Pei-Pei Li, Zhu-Hong You:
MNMDCDA: prediction of circRNA-disease associations by learning mixed neighborhood information from multiple distances. - Lijun Wu, Chengcan Yin, Jinhua Zhu, Zhen Wu, Liang He, Yingce Xia, Shufang Xie, Tao Qin, Tie-Yan Liu:
SPRoBERTa: protein embedding learning with local fragment modeling. - Caiwei Zhen, Yuxian Wang, Jiaquan Geng, Lu Han, Jingyi Li, Jinghao Peng, Tao Wang, Jianye Hao, Xuequn Shang, Zhongyu Wei, Peican Zhu, Jiajie Peng:
A review and performance evaluation of clustering frameworks for single-cell Hi-C data. - Xizhi Luo, Fei Qin, Feifei Xiao, Guoshuai Cai:
BISC: accurate inference of transcriptional bursting kinetics from single-cell transcriptomic data. - Yangyang Hao, Liang Lu, Anna Liu, Xue Lin, Li Xiao, Xiaoyue Kong, Kai Li, Fengji Liang, Jianghui Xiong, Lina Qu, Yinghui Li, Jian Li:
Integrating bioinformatic strategies in spatial life science research. - Yanzheng Meng, Lin Zhang, Laizhi Zhang, Ziyu Wang, Xuanwen Wang, Chan Li, Yu Chen, Shipeng Shang, Lei Li:
CysModDB: a comprehensive platform with the integration of manually curated resources and analysis tools for cysteine posttranslational modifications. - Xia-an Bi, Yuhua Mao, Sheng Luo, Hao Wu, Lixia Zhang, Xun Luo, Luyun Xu:
A novel generation adversarial network framework with characteristics aggregation and diffusion for brain disease classification and feature selection. - Zena Cai, Ping Fu, Ye Qiu, Aiping Wu, Gaihua Zhang, Yirong Wang, Taijiao Jiang, Xing-Yi Ge, Haizhen Zhu, Yousong Peng:
vsRNAfinder: a novel method for identifying high-confidence viral small RNAs from small RNA-Seq data. - Yiming Fang, Xuejun Liu, Hui Liu:
Attention-aware contrastive learning for predicting T cell receptor-antigen binding specificity. - Julien Tremblay, Lars Schreiber, Charles W. Greer:
High-resolution shotgun metagenomics: the more data, the better? - Yi Zhou, Xinyi Wang, Lin Yao, Min Zhu:
LDAformer: predicting lncRNA-disease associations based on topological feature extraction and Transformer encoder. - Chong Jin, Brian Lee, Li Shen, Qi Long:
Integrating multi-omics summary data using a Mendelian randomization framework. - Bo-Wei Zhao, Xiao-Rui Su, Peng-Wei Hu, Yu-Peng Ma, Xi Zhou, Lun Hu:
A geometric deep learning framework for drug repositioning over heterogeneous information networks. - Tang Li, Yanbin Yin:
Critical assessment of pan-genomic analysis of metagenome-assembled genomes. - Junyan Song, Pei Fen Kuan:
A systematic assessment of cell type deconvolution algorithms for DNA methylation data. - Medha Pandey, P. Anoosha, Dhanusha Yesudhas, M. Michael Gromiha:
Identification of potential driver mutations in glioblastoma using machine learning. - Daniel Rivas-Barragan, Daniel Domingo-Fernández, Yojana Gadiya, David Healey:
Ensembles of knowledge graph embedding models improve predictions for drug discovery. - Caimao Zhou, Dejun Peng, Bo Liao, Ranran Jia, Fang-Xiang Wu:
ACP_MS: prediction of anticancer peptides based on feature extraction. - Meihong Gao, Shuhui Liu, Yang Qi, Xinpeng Guo, Xuequn Shang:
GAE-LGA: integration of multi-omics data with graph autoencoders to identify lncRNA-PCG associations. - Chengkui Zhao, Nan Xu, Jingwen Tan, Qi Cheng, Weixin Xie, Jiayu Xu, Zhenyu Wei, Jing Ye, Lei Yu, Weixing Feng:
ILGBMSH: an interpretable classification model for the shRNA target prediction with ensemble learning algorithm. - Li Huang, Li Zhang, Xing Chen:
Updated review of advances in microRNAs and complex diseases: towards systematic evaluation of computational models. - Qiao Ning, Zedong Qi, Yue Wang, Ansheng Deng, Chen Chen:
FCCCSR_Glu: a semi-supervised learning model based on FCCCSR algorithm for prediction of glutarylation sites. - Jiannan Yang, Zhen Li, William Ka Kei Wu, Shi Yu, Zhongzhi Xu, Qian Chu, Qingpeng Zhang:
Deep learning identifies explainable reasoning paths of mechanism of action for drug repurposing from multilayer biological network. - Linlin Zhuo, Bosheng Song, Yuansheng Liu, Zejun Li, Xiangzheng Fu:
Predicting ncRNA-protein interactions based on dual graph convolutional network and pairwise learning. - Lei Huang, Jiecong Lin, Rui Liu, Zetian Zheng, Lingkuan Meng, Xingjian Chen, Xiangtao Li, Ka-Chun Wong:
CoaDTI: multi-modal co-attention based framework for drug-target interaction annotation. - Hui Chong, Yuguo Zha, Qingyang Yu, Mingyue Cheng, Guangzhou Xiong, Nan Wang, Xinhe Huang, Shijuan Huang, Chuqing Sun, Sicheng Wu, Wei-Hua Chen, Luís Pedro Coelho, Kang Ning:
EXPERT: transfer learning-enabled context-aware microbial community classification. - Hang Cheng, Yuhong Sun, Qing Yang, Minggui Deng, Zhijian Yu, Gang Zhu, Jiuxin Qu, Lei Liu, Liang Yang, Yu Xia:
A rapid bacterial pathogen and antimicrobial resistance diagnosis workflow using Oxford nanopore adaptive sequencing method. - Dailin Gan, Guosheng Yin, Yan Dora Zhang:
The GR2D2 estimator for the precision matrices. - Hanxuan Cai, Huimin Zhang, Duancheng Zhao, Jingxing Wu, Ling Wang:
FP-GNN: a versatile deep learning architecture for enhanced molecular property prediction. - Benjamin Goudey, Nicholas Geard, Karin Verspoor, Justin Zobel:
Propagation, detection and correction of errors using the sequence database network. - Correction to: A comprehensive benchmarking of WGS-based deletion structural variant callers.
- Peihao Fan, Julia Kofler, Ying Ding, Michael Marks, Robert A. Sweet, Lirong Wang:
Efficacy difference of antipsychotics in Alzheimer's disease and schizophrenia: explained with network efficiency and pathway analysis methods. - Wenya Wang, Li Zhang, Jianqiang Sun, Qi Zhao, Jianwei Shuai:
Predicting the potential human lncRNA-miRNA interactions based on graph convolution network with conditional random field. - Jiaxin Fan, Yafei Lyu, Qihuang Zhang, Xuran Wang, Mingyao Li, Rui Xiao:
MuSiC2: cell-type deconvolution for multi-condition bulk RNA-seq data. - Tianyang Zhang, Qiang Tang, Fulei Nie, Qi Zhao, Wei Chen:
DeepLncPro: an interpretable convolutional neural network model for identifying long non-coding RNA promoters. - Jiacheng Leng, Ling-Yun Wu:
Interaction-based transcriptome analysis via differential network inference. - Oskar Hickl, Pedro Queirós, Paul Wilmes, Patrick May, Anna Heintz-Buschart:
binny: an automated binning algorithm to recover high-quality genomes from complex metagenomic datasets. - Li Peng, Yuan Tu, Li Huang, Yang Li, Xiangzheng Fu, Xiang Chen:
DAESTB: inferring associations of small molecule-miRNA via a scalable tree boosting model based on deep autoencoder. - Kai-Yao Huang, Hui-Ju Kao, Tzu-Hsiang Weng, Chia-Hung Chen, Shun-Long Weng:
iDVIP: identification and characterization of viral integrase inhibitory peptides. - Siyu Han, Xiao Yang, Hang Sun, Hu Yang, Qi Zhang, Cheng Peng, Wensi Fang, Ying Li:
LION: an integrated R package for effective prediction of ncRNA-protein interaction. - Vandana Ravindran, Jessica Wagoner, Paschalis Athanasiadis, Andreas B. Den Hartigh, Julia M. Sidorova, Aleksandr Ianevski, Susan L. Fink, Arnoldo Frigessi, Judith White, Stephen J. Polyak, Tero Aittokallio:
Discovery of host-directed modulators of virus infection by probing the SARS-CoV-2-host protein-protein interaction network. - Siqi Chen, Tiancheng Li, Luna Yang, Fei Zhai, Xiwei Jiang, Rongwu Xiang, Guixia Ling:
Artificial intelligence-driven prediction of multiple drug interactions. - Hock Chuan Yeo, Kumar Selvarajoo:
Machine learning alternative to systems biology should not solely depend on data. - Jian Ruan, Shuaishuai Xu, Ruyin Chen, Wenxin Qu, Qiong Li, Chanqi Ye, Wei Wu, Qi Jiang, Feifei Yan, Enhui Shen, Qinjie Chu, Yunlu Jia, Xiaochen Zhang, Wenguang Fu, Jinzhang Chen, Michael P. Timko, Peng Zhao, Longjiang Fan, Yifei Shen:
EMLI-ICC: an ensemble machine learning-based integration algorithm for metastasis prediction and risk stratification in intrahepatic cholangiocarcinoma. - Xiaohui Shi, Huajing Teng, Zhongsheng Sun:
An updated overview of experimental and computational approaches to identify non-canonical DNA/RNA structures with emphasis on G-quadruplexes and R-loops. - Anna Eames Seffernick, Krzysztof Mrózek, Deedra Nicolet, Richard M. Stone, Ann-Katherin Eisfeld, John C. Byrd, Kellie J. Archer:
High-dimensional genomic feature selection with the ordered stereotype logit model. - Yu Xu, Jiaxing Chen, Aiping Lyu, William K. Cheung, Lu Zhang:
dynDeepDRIM: a dynamic deep learning model to infer direct regulatory interactions using time-course single-cell gene expression data. - Yue Gao, Shipeng Shang, Shuang Guo, Xinyue Wang, Hanxiao Zhou, Yue Sun, Jing Gan, Yakun Zhang, Xia Li, Shangwei Ning, Yunpeng Zhang:
AgingBank: a manually curated knowledgebase and high-throughput analysis platform that provides experimentally supported multi-omics data relevant to aging in multiple species. - Peng Zhang, Shikui Tu, Wen Zhang, Lei Xu:
Predicting cell line-specific synergistic drug combinations through a relational graph convolutional network with attention mechanism. - Shamima Rashid, Teng Ann Ng, Chee Keong Kwoh:
Jupytope: computational extraction of structural properties of viral epitopes. - Yaojia Chen, Jiacheng Wang, Chuyu Wang, Mingxin Liu, Quan Zou:
Deep learning models for disease-associated circRNA prediction: a review. - Laura Marchetti, Riccardo Nifosì, Pier Luigi Martelli, Eleonora Da Pozzo, Valentina Cappello, Francesco Banterle, Maria Letizia Trincavelli, Claudia Martini, Massimo D'elia:
Quantum computing algorithms: getting closer to critical problems in computational biology. - Zeynab Maghsoudi, Ha Nguyen, Alireza Tavakkoli, Tin Nguyen:
A comprehensive survey of the approaches for pathway analysis using multi-omics data integration. - Meng Zhang, Jian Zhao, Chen Li, Fang Ge, Jing Wu, Bin Jiang, Jiangning Song, Xiaofeng Song:
csORF-finder: an effective ensemble learning framework for accurate identification of multi-species coding short open reading frames. - Su Wang, Miran Kim, Wentao Li, Xiaoqian Jiang, Han Chen, Arif Ozgun Harmanci:
Privacy-aware estimation of relatedness in admixed populations. - Renqian Luo, Liai Sun, Yingce Xia, Tao Qin, Sheng Zhang, Hoifung Poon, Tie-Yan Liu:
BioGPT: generative pre-trained transformer for biomedical text generation and mining.
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