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ORCIDBriefings in Bioinformatics, Volume 24
Volume 24, Number 1, January 2023
- Jiung-Wen Chen
, Lisa Shrestha, George Green, André Leier, Tatiana T. Marquez-Lago:
The hitchhikers' guide to RNA sequencing and functional analysis. - Jiacheng Lin, Lijun Wu, Jinhua Zhu, Xiaobo Liang, Yingce Xia, Shufang Xie, Tao Qin, Tie-Yan Liu:
R2-DDI: relation-aware feature refinement for drug-drug interaction prediction. - Jing Wang, Junfeng Xia, Haiyun Wang, Yansen Su, Chun-Hou Zheng:
scDCCA: deep contrastive clustering for single-cell RNA-seq data based on auto-encoder network. - Eva María Trinidad, Enrique Vidal, Esther Coronado, Anna Esteve-Codina, Victoria Castel, Adela Cañete Nieto, Marta Gut, Simon Heath, Jaime Font de Mora:
Liquidhope: methylome and genomic profiling from very limited quantities of plasma-derived DNA. - Correction to: siGCD: a web server to explore survival interaction of genes, cells and drugs in human cancers.
- Yue-Hua Feng, Shao-Wu Zhang, Yi-Yang Feng, Qing-Qing Zhang, Ming-Hui Shi, Jian-Yu Shi:
A social theory-enhanced graph representation learning framework for multitask prediction of drug-drug interactions. - Xiayang Li, Moxuan Chen, Huaming Wu:
Multiple errors correction for position-limited DNA sequences with GC balance and no homopolymer for DNA-based data storage. - Chengqian Lu, Lishen Zhang, Min Zeng, Wei Lan, Guihua Duan, Jianxin Wang:
Inferring disease-associated circRNAs by multi-source aggregation based on heterogeneous graph neural network. - Ziyang Tang, Tonglin Zhang, Baijian Yang, Jing Su, Qianqian Song:
spaCI: deciphering spatial cellular communications through adaptive graph model. - Bailing Zhou, Maolin Ding, Jing Feng, Baohua Ji, Pingping Huang, Junye Zhang, Xue Yu, Zanxia Cao, Yuedong Yang, Yaoqi Zhou, Jihua Wang:
EVlncRNA-Dpred: improved prediction of experimentally validated lncRNAs by deep learning. - Yimeng Wang, Mengting Huang, Hua Deng, Weihua Li, Zengrui Wu, Yun Tang, Guixia Liu:
Identification of vital chemical information via visualization of graph neural networks. - Ilias Moutsopoulos, Eleanor C. Williams, Irina Mohorianu:
bulkAnalyseR: an accessible, interactive pipeline for analysing and sharing bulk multi-modal sequencing data. - Yuanyuan Chen, Hao Zhang, Xiao Sun:
Improving the performance of single-cell RNA-seq data mining based on relative expression orderings. - Ryley Dorney, Bijay P. Dhungel, John E. J. Rasko, Lionel Hebbard, Ulf Schmitz:
Recent advances in cancer fusion transcript detection. - Lei Wang, Shao-Hua Shi, Hui Li, Xiangxiang Zeng, Su-You Liu, Zhao-Qian Liu, Yafeng Deng, Ai-Ping Lu, Tingjun Hou, Dong-Sheng Cao:
Reducing false positive rate of docking-based virtual screening by active learning. - Khandakar Tanvir Ahmed, Sze Cheng, Qian Li, Jeongsik Yong, Wei Zhang:
Incomplete time-series gene expression in integrative study for islet autoimmunity prediction. - Denis Beslic, Georg Tscheuschner, Bernhard Y. Renard, Michael G. Weller, Thilo Muth:
Comprehensive evaluation of peptide de novo sequencing tools for monoclonal antibody assembly. - Jun Liu, Kai-Long Zhao, Guijun Zhang:
Improved model quality assessment using sequence and structural information by enhanced deep neural networks. - Wenjie Du, Xiaoting Yang, Di Wu, Fenfen Ma, Baicheng Zhang, Chaochao Bao, Yaoyuan Huo, Jun Jiang, Xin Chen, Yang Wang:
Fusing 2D and 3D molecular graphs as unambiguous molecular descriptors for conformational and chiral stereoisomers. - Jiayin Zhou, Wen Song, Qichao Tu:
To assemble or not to assemble: metagenomic profiling of microbially mediated biogeochemical pathways in complex communities. - Wayland Yeung, Zhongliang Zhou, Liju Mathew, Nathan Gravel, Rahil Taujale, Brady O'boyle, Mariah Salcedo, Aarya Venkat, William Lanzilotta, Sheng Li, Natarajan Kannan:
Tree visualizations of protein sequence embedding space enable improved functional clustering of diverse protein superfamilies. - Wayland Yeung, Zhongliang Zhou, Sheng Li, Natarajan Kannan:
Alignment-free estimation of sequence conservation for identifying functional sites using protein sequence embeddings. - Vandana Bharti, Shabari S. Nair, Akshat Jain, Kaushal Kumar Shukla, Bhaskar Biswas:
Concept drift detection in toxicology datasets using discriminative subgraph-based drift detector. - Fang Ge, Chen Li, Shahid Iqbal, Muhammad Arif, Fuyi Li, Maha A. Thafar, Zihao Yan, Apilak Worachartcheewan, Xiaofeng Xu, Jiangning Song, Dong-Jun Yu:
VPatho: a deep learning-based two-stage approach for accurate prediction of gain-of-function and loss-of-function variants. - Kai Yu, Zekun Liu, Haoyang Cheng, Shihua Li, Qingfeng Zhang, Jia Liu, Huai-Qiang Ju, Zhixiang Zuo, Qi Zhao, Shiyang Kang, Zexian Liu:
dSCOPE: a software to detect sequences critical for liquid-liquid phase separation. - Xing Chen, Li Huang:
Computational model for disease research. - Yanan Tian, Xiaorui Wang, Xiaojun Yao, Huanxiang Liu, Ying Yang:
Predicting molecular properties based on the interpretable graph neural network with multistep focus mechanism. - Danni Hong, Hongli Lin, Lifang Liu, Muya Shu, Jianwu Dai, Falong Lu, Mengsha Tong, Jialiang Huang:
Complexity of enhancer networks predicts cell identity and disease genes revealed by single-cell multi-omics analysis. - Lishen Zhang, Chengqian Lu, Min Zeng, Yaohang Li, Jianxin Wang:
CRMSS: predicting circRNA-RBP binding sites based on multi-scale characterizing sequence and structure features. - Zheng Zhang, Shengquan Chen, Zhixiang Lin:
RefTM: reference-guided topic modeling of single-cell chromatin accessibility data. - Sayed-Rzgar Hosseini, Xiaobo Zhou:
CCSynergy: an integrative deep-learning framework enabling context-aware prediction of anti-cancer drug synergy. - Kai Zheng, Xin-Lu Zhang, Lei Wang, Zhu-Hong You, Bo-Ya Ji, Xiao Liang, Zheng-Wei Li:
SPRDA: a link prediction approach based on the structural perturbation to infer disease-associated Piwi-interacting RNAs. - Wenchuang He, Kunli Xiang, Caijin Chen, Jie Wang, Zhiqiang Wu:
Master graph: an essential integrated assembly model for the plant mitogenome based on a graph-based framework. - Nishant Kumar, Sumeet Patiyal, Shubham Choudhury, Ritu Tomer, Anjali Dhall, Gajendra P. S. Raghava:
DMPPred: a tool for identification of antigenic regions responsible for inducing type 1 diabetes mellitus. - Ke Han, Jianchun Wang, Yu Wang, Lei Zhang, Mengyao Yu, Fang Xie, Dequan Zheng, Yaoqun Xu, Yijie Ding, Jie Wan:
A review of methods for predicting DNA N6-methyladenine sites. - Yishu Wang, Juan Qi, Dongmei Ai:
DPADM: a novel algorithm for detecting drug-pathway associations based on high-throughput transcriptional response to compounds. - Xue Li, Peifu Han, Wenqi Chen, Changnan Gao, Shuang Wang, Tao Song, Muyuan Niu, Alfonso Rodríguez-Patón:
MARPPI: boosting prediction of protein-protein interactions with multi-scale architecture residual network. - Bo Song, Hao Li, Mengyun Jiang, Zhongtian Gao, Suikang Wang, Lei Gao, Yunsheng Chen, Wujiao Li:
slORFfinder: a tool to detect open reading frames resulting from trans-splicing of spliced leader sequences. - Hai Yang, Lipeng Gan, Rui Chen, Dongdong Li, Jing Zhang, Zhe Wang:
From multi-omics data to the cancer druggable gene discovery: a novel machine learning-based approach. - Wubin Ding, Diljeet Kaur, Steve Horvath, Wanding Zhou:
Comparative epigenome analysis using Infinium DNA methylation BeadChips. - Kaili Wang, Renyi Zhou, Yifan Wu, Min Li:
RLBind: a deep learning method to predict RNA-ligand binding sites. - Yifang Wei, Lingmei Li, Xin Zhao, Haitao Yang, Jian Sa, Hongyan Cao, Yuehua Cui:
Cancer subtyping with heterogeneous multi-omics data via hierarchical multi-kernel learning. - Simon Orozco-Arias, Luis Humberto López-Murillo, Mariana S. Candamil-Cortes, Maradey Arias, Paula A. Jaimes, Alexandre Rossi Paschoal, Reinel Tabares-Soto, Gustavo A. Isaza, Romain Guyot:
Inpactor2: a software based on deep learning to identify and classify LTR-retrotransposons in plant genomes. - Lihua Wang, Tao Zhang, Lihong Yu, Chun-Hou Zheng, Wenguang Yin, Junfeng Xia, Tiejun Zhang:
Deleterious synonymous mutation identification based on selective ensemble strategy. - Ze-Qun Zhang, Junlin Xu, Yanan Wu, Nian-Nian Liu, Ying-Long Wang, Ying Liang:
CapsNet-LDA: predicting lncRNA-disease associations using attention mechanism and capsule network based on multi-view data. - Yiming Xu, Bowen Zheng, Xiaohong Liu, Tao Wu, Jinxiu Ju, Shi-jie Wang, Yufan Lian, Hongjun Zhang, Tong Liang, Ye Sang, Rui Jiang, Guangyu Wang, Jie Ren, Ting Chen:
Improving artificial intelligence pipeline for liver malignancy diagnosis using ultrasound images and video frames. - Gabriel Cia, Fabrizio Pucci, Marianne Rooman:
Critical review of conformational B-cell epitope prediction methods. - Siqin Zhang, Kuo Yang, Zhenhong Liu, Xinxing Lai, Zhen Yang, Jianyang Zeng, Shao Li:
DrugAI: a multi-view deep learning model for predicting drug-target activating/inhibiting mechanisms. - Shu-Guang Ge, Jian Liu, Yuhu Cheng, Xiaojing Meng, Xuesong Wang:
Multi-view spectral clustering with latent representation learning for applications on multi-omics cancer subtyping. - Huiyu Li, Chen Shen, Gongji Wang, Qinru Sun, Kai Yu, Zefeng Li, Xinggong Liang, Run Chen, Hao Wu, Fan Wang, Zhenyuan Wang, Chunfeng Lian:
BloodNet: An attention-based deep network for accurate, efficient, and costless bloodstain time since deposition inference. - Donghyo Kim, Doyeon Ha, Kwanghwan Lee, Heetak Lee, Inhae Kim, Sanguk Kim:
An evolution-based machine learning to identify cancer type-specific driver mutations. - Zhangxin Zhao, Qianjin Feng, Yu Zhang, Zhenyuan Ning:
Adaptive risk-aware sharable and individual subspace learning for cancer survival analysis with multi-modality data. - Zhiqiang Hu, Wenfeng Liu, Chenbin Zhang, Jiawen Huang, Shaoting Zhang, Huiqun Yu, Yi Xiong, Hao Liu, Song Ke, Liang Hong:
SAM-DTA: a sequence-agnostic model for drug-target binding affinity prediction. - Jianbo Fu, Qingxia Yang, Yongchao Luo, Song Zhang, Jing Tang, Ying Zhang, Hongning Zhang, Hanxiang Xu, Feng Zhu:
Label-free proteome quantification and evaluation. - Qi Liang, Wenxiang Zhang, Hao Wu, Bin Liu:
LncRNA-disease association identification using graph auto-encoder and learning to rank. - Alessia Buratin, Stefania Bortoluzzi, Enrico Gaffo:
Systematic benchmarking of statistical methods to assess differential expression of circular RNAs. - Risa Karakida Kawaguchi, Ziqi Tang, Stephan Fischer, Chandana Rajesh, Rohit Tripathy, Peter K. Koo, Jesse A. Gillis:
Learning single-cell chromatin accessibility profiles using meta-analytic marker genes. - Hailin Feng, Dongdong Jin, Jian Li, Yane Li, Quan Zou, Tongcun Liu:
Matrix reconstruction with reliable neighbors for predicting potential MiRNA-disease associations. - Bihan Shen, Fangyoumin Feng, Kunshi Li, Ping Lin, Liangxiao Ma, Hong Li:
A systematic assessment of deep learning methods for drug response prediction: from in vitro to clinical applications. - Ruyun Hu, Lihao Fu, Yongcan Chen, Junyu Chen, Yu Qiao, Tong Si:
Protein engineering via Bayesian optimization-guided evolutionary algorithm and robotic experiments. - Mengqi Luo, Shangfu Li, Yuxuan Pang, Lantian Yao, Renfei Ma, Hsi-Yuan Huang, Hsien-Da Huang, Tzong-Yi Lee:
Extraction of microRNA-target interaction sentences from biomedical literature by deep learning approach. - Zechen Wang, Liangzhen Zheng, Sheng Wang, Mingzhi Lin, Zhihao Wang, Adams Wai-Kin Kong, Yuguang Mu, Yanjie Wei, Weifeng Li:
A fully differentiable ligand pose optimization framework guided by deep learning and a traditional scoring function. - Yu Zhao, Bing He, Zhimeng Xu, Yidan Zhang, Xuan Zhao, Zhi-An Huang, Fan Yang, Liang Wang, Lei Duan, Jiangning Song, Jianhua Yao:
Interpretable artificial intelligence model for accurate identification of medical conditions using immune repertoire. - Erik Christensen, Ping Luo, Andrei L. Turinsky, Mia Husic, Alaina Mahalanabis, Alaine Naidas, Juan Javier Díaz-Mejía, Michael Brudno, Trevor J. Pugh, Arun K. Ramani, Parisa Shooshtari:
Evaluation of single-cell RNAseq labelling algorithms using cancer datasets. - Xiaosa Zhao, Jun Wu, Xiaowei Zhao, Minghao Yin:
Multi-view contrastive heterogeneous graph attention network for lncRNA-disease association prediction. - Lei Sun, Gongming Wang, Zhihua Zhang:
SimCH: simulation of single-cell RNA sequencing data by modeling cellular heterogeneity at gene expression level. - Xinyi Xu, Xiangjie Li:
Structure-preserved dimension reduction using joint triplets sampling for multi-batch integration of single-cell transcriptomic data. - Lu Yang, Jun Chen:
Benchmarking differential abundance analysis methods for correlated microbiome sequencing data. - Chun-Jie Liu, Fei-Fei Hu, Gui-Yan Xie, Ya-Ru Miao, Xin-Wen Li, Yan Zeng, An-Yuan Guo:
GSCA: an integrated platform for gene set cancer analysis at genomic, pharmacogenomic and immunogenomic levels. - Zerun Lin, Le Ou-Yang:
Inferring gene regulatory networks from single-cell gene expression data via deep multi-view contrastive learning. - Chonghao Wang, Jing Zhang, Werner Pieter Veldsman, Xin Zhou, Lu Zhang:
A comprehensive investigation of statistical and machine learning approaches for predicting complex human diseases on genomic variants. - Yitian Fang, Fan Xu, Lesong Wei, Yi Jiang, Jie Chen, Leyi Wei, Dong-Qing Wei:
AFP-MFL: accurate identification of antifungal peptides using multi-view feature learning. - Correction to: VPatho: a deep learning-based two-stage approach for accurate prediction of gain-of-function and loss-of-function variants.
- George Minadakis, Marios Tomazou, Nikolas Dietis, George M. Spyrou:
Vir2Drug: a drug repurposing framework based on protein similarities between pathogens. - Andrew Cheng, Guanyu Hu, Wei Vivian Li:
Benchmarking cell-type clustering methods for spatially resolved transcriptomics data. - Wujuan Zhong, Aparna Chhibber, Lan Luo, Devan V. Mehrotra, Judong Shen:
A fast and powerful linear mixed model approach for genotype-environment interaction tests in large-scale GWAS. - Yang Yue, Yongxuan Liu, Luoying Hao, Huangshu Lei, Shan He:
Improving therapeutic synergy score predictions with adverse effects using multi-task heterogeneous network learning. - Marta Nazzari, Duncan Hauser, Marcel van Herwijnen, Mírian Romitti, Daniel J. Carvalho, Anna M. Kip, Florian Caiment:
CODA: a combo-Seq data analysis workflow. - Huan Hu, Zhen Feng, Hai Lin, Junjie Zhao, Yaru Zhang, Fei Xu, Lingling Chen, Feng Chen, Yunlong Ma, Jianzhong Su, Qi Zhao, Jianwei Shuai:
Modeling and analyzing single-cell multimodal data with deep parametric inference. - Sumeet Patiyal, Anjali Dhall, Khushboo Bajaj, Harshita Sahu, Gajendra P. S. Raghava:
Prediction of RNA-interacting residues in a protein using CNN and evolutionary profile. - Yi-Zhou He, Yue Yang, Xiao-Rui Su, Bo-Wei Zhao, Shengwu Xiong, Lun Hu:
Incorporating higher order network structures to improve miRNA-disease association prediction based on functional modularity. - Zimeng Li, Shichao Zhu, Bin Shao, Xiangxiang Zeng, Tong Wang, Tie-Yan Liu:
DSN-DDI: an accurate and generalized framework for drug-drug interaction prediction by dual-view representation learning. - Lifang Yang, Ye Yang, Luqi Huang, Xiuming Cui, Yuan Liu:
From single- to multi-omics: future research trends in medicinal plants. - Joel Defo, Denis Awany, Raj Ramesar:
From SNP to pathway-based GWAS meta-analysis: do current meta-analysis approaches resolve power and replication in genetic association studies? - Karel K. M. Koudijs, Stefan Böhringer, Henk-Jan Guchelaar:
Validation of transcriptome signature reversion for drug repurposing in oncology. - Hai-Yun Wang, Jian-Ping Zhao, Chun-Hou Zheng, Yan-Sen Su:
scGMAAE: Gaussian mixture adversarial autoencoders for diversification analysis of scRNA-seq data. - Marwan Abdellah, Juan Jose Garcia-Cantero, Nadir Román Guerrero, Alessandro Foni, Jay S. Coggan, Corrado Calì, Marco Agus, Eleftherios Zisis, Daniel Keller, Markus Hadwiger, Pierre J. Magistretti, Henry Markram, Felix Schürmann:
Ultraliser: a framework for creating multiscale, high-fidelity and geometrically realistic 3D models for in silico neuroscience. - Hong Wang, Chong Dai, Yuqi Wen, Xiaoqi Wang, Wenjuan Liu, Song He, Xiaochen Bo, Shaoliang Peng:
GADRP: graph convolutional networks and autoencoders for cancer drug response prediction. - Dong Wang, Zhenxing Wu, Chao Shen, Lingjie Bao, Hao Luo, Zhe Wang, Hucheng Yao, De-Xin Kong, Cheng Luo, Tingjun Hou:
Learning with uncertainty to accelerate the discovery of histone lysine-specific demethylase 1A (KDM1A/LSD1) inhibitors. - Tianyuan Liu, Bohao Zou, Manman He, Yongfei Hu, Yiying Dou, Tianyu Cui, Puwen Tan, Shaobin Li, Shuan Rao, Yan Huang, Sixi Liu, Kaican Cai, Dong Wang:
LncReader: identification of dual functional long noncoding RNAs using a multi-head self-attention mechanism. - Muhammad Nabeel Asim, Muhammad Ali Ibrahim, Ahtisham Fazeel, Andreas Dengel, Sheraz Ahmed:
DNA-MP: a generalized DNA modifications predictor for multiple species based on powerful sequence encoding method. - Chun-Qiu Xia, Shi-Hao Feng, Ying Xia, Xiaoyong Pan, Hong-Bin Shen:
Leveraging scaffold information to predict protein-ligand binding affinity with an empirical graph neural network. - Yongchao Luo, Panpan Wang, Minjie Mou, Hanqi Zheng, Jiajun Hong, Lin Tao, Feng Zhu:
A novel strategy for designing the magic shotguns for distantly related target pairs. - Jingxuan Zhao, Jianqiang Sun, Stella C. Shuai, Qi Zhao, Jianwei Shuai:
Predicting potential interactions between lncRNAs and proteins via combined graph auto-encoder methods. - Lingling Wang, Lei Xu, Zhe Wang, Tingjun Hou, Haiping Hao, Huiyong Sun:
Cooperation of structural motifs controls drug selectivity in cyclin-dependent kinases: an advanced theoretical analysis. - Softya Sebastian, Swarup Roy, Jugal Kalita:
A generic parallel framework for inferring large-scale gene regulatory networks from expression profiles: application to Alzheimer's disease network. - Jing-Zhe Jiang, Wen-Guang Yuan, Jiayu Shang, Ying-Hui Shi, Li-Ling Yang, Min Liu, Peng Zhu, Tao Jin, Yanni Sun, Li-Hong Yuan:
Virus classification for viral genomic fragments using PhaGCN2. - Zhi-Jian Zhou, Chen-Hui Yang, Sheng-Bao Ye, Xiao-Wei Yu, Ye Qiu, Xing-Yi Ge:
VirusRecom: an information-theory-based method for recombination detection of viral lineages and its application on SARS-CoV-2. - Yanqiang Han, Zhilong Wang, An Chen, Imran Ali, Junfei Cai, Simin Ye, Zhiyun Wei, Jin-Jin Li:
A deep transfer learning-based protocol accelerates full quantum mechanics calculation of protein. - Cheng-Hong Yang, Ming-Feng Hou, Li-Yeh Chuang, Cheng-San Yang, Yu-Da Lin:
Dimensionality reduction approach for many-objective epistasis analysis. - Han Yu, Xiaozhou Luo:
IPPF-FE: an integrated peptide and protein function prediction framework based on fused features and ensemble models. - Yang Yang, Yuwei Lu, Wenying Yan:
A comprehensive review on knowledge graphs for complex diseases. - Nisha Bajiya, Anjali Dhall, Suchet Aggarwal, Gajendra P. S. Raghava:
Advances in the field of phage-based therapy with special emphasis on computational resources. - Zhenshuang Tang, Lilin Yin, Dong Yin, Haohao Zhang, Yuhua Fu, Guangliang Zhou, Yunxiang Zhao, Zhiquan Wang, Xiaolei Liu, Xinyun Li, Shuhong Zhao:
Development and application of an efficient genomic mating method to maximize the production performances of three-way crossbred pigs. - Siqi Chen, Xuhua Yan, Ruiqing Zheng, Min Li:
Bubble: a fast single-cell RNA-seq imputation using an autoencoder constrained by bulk RNA-seq data. - Qilong Wu, Sheng-You Huang:
HCovDock: an efficient docking method for modeling covalent protein-ligand interactions. - Zixi Zheng, Yanyan Tan, Hong Wang, Shengpeng Yu, Tianyu Liu, Cheng Liang:
CasANGCL: pre-training and fine-tuning model based on cascaded attention network and graph contrastive learning for molecular property prediction. - Jing Liu, Xinghua Tang, Xiao Guan:
Grain protein function prediction based on self-attention mechanism and bidirectional LSTM. - Qitong Yuan, Keyi Chen, Yimin Yu, Nguyen-Quoc-Khanh Le, Matthew Chin Heng Chua:
Prediction of anticancer peptides based on an ensemble model of deep learning and machine learning using ordinal positional encoding. - Kai Wang:
GPDOCK: highly accurate docking strategy for metalloproteins based on geometric probability. - Haohong Zhang, Hui Chong, Qingyang Yu, Yuguo Zha, Mingyue Cheng, Kang Ning:
Tracing human life trajectory using gut microbial communities by context-aware deep learning. - Wengang Wang, Hailin Chen:
Predicting miRNA-disease associations based on lncRNA-miRNA interactions and graph convolution networks. - Tian-Hao Li, Chun-Chun Wang, Li Zhang, Xing Chen:
SNRMPACDC: computational model focused on Siamese network and random matrix projection for anticancer synergistic drug combination prediction. - Zhenling Peng, Zixia Li, Qiaozhen Meng, Bi Zhao, Lukasz A. Kurgan:
CLIP: accurate prediction of disordered linear interacting peptides from protein sequences using co-evolutionary information. - Mei Li, Xiangrui Cai, Sihan Xu, Hua Ji:
Metapath-aggregated heterogeneous graph neural network for drug-target interaction prediction. - Guanqun Meng, Wen Tang, Emina Huang, Ziyi Li, Hao Feng:
A comprehensive assessment of cell type-specific differential expression methods in bulk data. - Shuaiqun Wang, Kai Zheng, Wei Kong, Ruiwen Huang, Lulu Liu, Gen Wen, Yaling Yu:
Multimodal data fusion based on IGERNNC algorithm for detecting pathogenic brain regions and genes in Alzheimer's disease. - Guobo Xie, Rui-Bin Chen, Zhiyi Lin, Guosheng Gu, Jun-Rui Yu, Zhen-Guo Liu, Ji Cui, Lieqing Lin, Lang-Cheng Chen:
Predicting lncRNA-disease associations based on combining selective similarity matrix fusion and bidirectional linear neighborhood label propagation. - Yingjun Ma, Junjiang Zhong:
Logistic tensor decomposition with sparse subspace learning for prediction of multiple disease types of human-virus protein-protein interactions. - Anna Dal Molin, Caterina Tretti Parenzan, Enrico Gaffo, Cristina Borin, Elena Boldrin, Lueder H. Meyer, Geertruij te Kronnie, Silvia Bresolin, Stefania Bortoluzzi:
Discovery of fusion circular RNAs in leukemia with KMT2A::AFF1 rearrangements by the new software CircFusion. - Shehab Sarar Ahmed, Zaara Tasnim Rifat, M. Saifur Rahman, M. Sohel Rahman:
Succinylated lysine residue prediction revisited. - Jiayu Shang, Xubo Tang, Yanni Sun:
PhaTYP: predicting the lifestyle for bacteriophages using BERT. - Hongwei Chen, Yunpeng Cai, Chaojie Ji, Gurudeeban Selvaraj, Dongqing Wei, Hongyan Wu:
AdaPPI: identification of novel protein functional modules via adaptive graph convolution networks in a protein-protein interaction network. - Giulia Pais, Giulio Spinozzi, Daniela Cesana, Fabrizio Benedicenti, Alessandra Albertini, Maria Ester Bernardo, Bernhard Gentner, Eugenio Montini, Andrea Calabria:
ISAnalytics enables longitudinal and high-throughput clonal tracking studies in hematopoietic stem cell gene therapy applications. - Yetian Fan, April S. Chan, Jun Zhu, Suet Yi Leung, Xiaodan Fan:
A Bayesian model for identifying cancer subtypes from paired methylation profiles. - Min Li, Baoying Zhao, Rui Yin, Chengqian Lu, Fei Guo, Min Zeng:
GraphLncLoc: long non-coding RNA subcellular localization prediction using graph convolutional networks based on sequence to graph transformation. - Weibin Huang, Yuhui Zhang, Songyao Chen, Haofan Yin, Guangyao Liu, Huaqi Zhang, Jiannan Xu, Jishang Yu, Yujian Xia, Yulong He, Changhua Zhang:
Personalized immune subtypes based on machine learning predict response to checkpoint blockade in gastric cancer. - Qiao Liu, Wanwen Zeng, Wei Zhang, Sicheng Wang, Hongyang Chen, Rui Jiang, Mu Zhou, Shaoting Zhang:
Deep generative modeling and clustering of single cell Hi-C data. - Yan Kang, Yulong Xu, Xinchao Wang, Bin Pu, Xuekun Yang, Yulong Rao, Jianguo Chen:
HN-PPISP: a hybrid network based on MLP-Mixer for protein-protein interaction site prediction. - Yao Lu, Zhiqiang Pang, Jianguo Xia:
Comprehensive investigation of pathway enrichment methods for functional interpretation of LC-MS global metabolomics data. - Kanggeun Lee, Dongbin Cho, Jinho Jang, Kang Choi, Hyoung-oh Jeong, Jiwon Seo, Won-Ki Jeong, Semin Lee:
RAMP: response-aware multi-task learning with contrastive regularization for cancer drug response prediction. - Bo Yang, Hailin Chen:
Predicting circRNA-drug sensitivity associations by learning multimodal networks using graph auto-encoders and attention mechanism. - Wenxuan Deng, Bolun Li, Jiawei Wang, Wei Jiang, Xiting Yan, Ningshan Li, Milica Vukmirovic, Naftali Kaminski, Jing Wang, Hongyu Zhao:
A novel Bayesian framework for harmonizing information across tissues and studies to increase cell type deconvolution accuracy. - Upendra Kumar Pradhan, Prabina Kumar Meher, Sanchita Naha, Soumen Pal, Ajit Gupta, Rajender Parsad:
PlDBPred: a novel computational model for discovery of DNA binding proteins in plants. - Peicong Lin, Yumeng Yan, Sheng-You Huang:
DeepHomo2.0: improved protein-protein contact prediction of homodimers by transformer-enhanced deep learning. - Mengjie Shao, Nanjiao Ying, Qian Liang, Nan Ma, Sebastian Leptihn, Yunsong Yu, Huan Chen, Chengzhi Liu, Xiaoting Hua:
Pdif-mediated antibiotic resistance genes transfer in bacteria identified by pdifFinder. - Sanghun Lee, Georg Hahn, Julian Hecker, Sharon Marie Lutz, Kristina Mullin, Winston Hide, Lars Bertram, Dawn L. Demeo, Rudolph E. Tanzi, Christoph Lange, Dmitry Prokopenko:
A comparison between similarity matrices for principal component analysis to assess population stratification in sequenced genetic data sets. - Tim D. Rose, Nikolai Köhler, Lisa Falk, Lucie Klischat, Olga Lazareva, Josch Konstantin Pauling:
Lipid network and moiety analysis for revealing enzymatic dysregulation and mechanistic alterations from lipidomics data. - Wei Lan, Yi Dong, Hongyu Zhang, Chunling Li, Qingfeng Chen, Jin Liu, Jianxin Wang, Yi-Ping Phoebe Chen:
Benchmarking of computational methods for predicting circRNA-disease associations. - Xiao Fan, Hongbing Pan, Alan Tian, Wendy K. Chung, Yufeng Shen:
SHINE: protein language model-based pathogenicity prediction for short inframe insertion and deletion variants. - Haoran Gong, Jianguo Wen, Ruihan Luo, Yuzhou Feng, Jingjing Guo, Hongguang Fu, Xiaobo Zhou:
Integrated mRNA sequence optimization using deep learning. - Xi-Liang Zhu, Lin-Xia Bao, Min-Qi Xue, Ying-Ying Xu:
Automatic recognition of protein subcellular location patterns in single cells from immunofluorescence images based on deep learning. - Shujun He, Baizhen Gao, Rushant Sabnis, Qing Sun:
RNAdegformer: accurate prediction of mRNA degradation at nucleotide resolution with deep learning. - Guo-Hua Yuan, Ying Wang, Guang-Zhong Wang, Li Yang:
RNAlight: a machine learning model to identify nucleotide features determining RNA subcellular localization. - Xin Zhang, Lesong Wei, Xiucai Ye, Kai Zhang, Saisai Teng, Zhongshen Li, Junru Jin, Min Jae Kim, Tetsuya Sakurai, Lizhen Cui, Balachandran Manavalan, Leyi Wei:
SiameseCPP: a sequence-based Siamese network to predict cell-penetrating peptides by contrastive learning. - Kuo Yang, Yuxia Yang, Shuyue Fan, Jianan Xia, Qiguang Zheng, Xin Dong, Jun Liu, Qiong Liu, Lei Lei, Yingying Zhang, Bing Li, Zhuye Gao, Runshun Zhang, Baoyan Liu, Zhong Wang, Xuezhong Zhou:
DRONet: effectiveness-driven drug repositioning framework using network embedding and ranking learning.
Volume 24, Number 2, March 2023
- Xuebin Wang, Taifu Wang, Zhihao Xie, Youjin Zhang, Shiqiang Xia, Ruixue Sun, Xinqiu He, Ruizhi Xiang, Qiwen Zheng, Zhencheng Liu, Jin'an Wang, Honglong Wu, Xiangqian Jin, Weijun Chen, Dongfang Li, Zengquan He:
GPMeta: a GPU-accelerated method for ultrarapid pathogen identification from metagenomic sequences. - Yuepeng Jiang, Shuai Cheng Li:
Deep autoregressive generative models capture the intrinsics embedded in T-cell receptor repertoires. - Yuepeng Jiang, Miaozhe Huo, Shuai Cheng Li:
TEINet: a deep learning framework for prediction of TCR-epitope binding specificity. - Lingyu Cui, Hongfei Li, Jilong Bian, Guohua Wang, Yingjian Liang:
Unsupervised construction of gene regulatory network based on single-cell multi-omics data of colorectal cancer. - Yuyao Zhai, Liang Chen, Minghua Deng:
scGAD: a new task and end-to-end framework for generalized cell type annotation and discovery. - Lei Wang, Chen Huang, Mingxia Wang, Zhidong Xue, Yan Wang:
NeuroPred-PLM: an interpretable and robust model for neuropeptide prediction by protein language model. - Ming-Xiao Zhao, Qiang Chen, Fulai Li, Songsen Fu, Biling Huang, Yufen Zhao:
Protein phosphorylation database and prediction tools. - Xingzhong Zhao, Anyi Yang, Zi-Chao Zhang, Yucheng T. Yang, Xing-Ming Zhao:
Deciphering the genetic architecture of human brain structure and function: a brief survey on recent advances of neuroimaging genomics. - Rebecca Elizabeth Kattan, Deena Ayesh, Wenqi Wang:
Analysis of affinity purification-related proteomic data for studying protein-protein interaction networks in cells. - Yan Huang, Stefan Wuchty, Yuan Zhou, Ziding Zhang:
SGPPI: structure-aware prediction of protein-protein interactions in rigorous conditions with graph convolutional network. - Pei-Dong Zhang, Chun-Qiu Xia, Hong-Bin Shen:
High-accuracy protein model quality assessment using attention graph neural networks. - Jing Zhao, Bowen Zhao, Xiaotong Song, Chujun Lyu, Weizhi Chen, Yi Xiong, Dong-Qing Wei:
Subtype-DCC: decoupled contrastive clustering method for cancer subtype identification based on multi-omics data. - Zhen Tian, Yue Yu, Haichuan Fang, Weixin Xie, Maozu Guo:
Predicting microbe-drug associations with structure-enhanced contrastive learning and self-paced negative sampling strategy. - Jianchang Hu, Silke Szymczak:
A review on longitudinal data analysis with random forest. - Peiran Jiang, Ruoxi Cai, Jose Lugo-Martinez, Yaping Guo:
A hybrid positive unlabeled learning framework for uncovering scaffolds across human proteome by measuring the propensity to drive phase separation. - Yunda Si, Chengfei Yan:
Improved inter-protein contact prediction using dimensional hybrid residual networks and protein language models. - Marc Oeller, Ryan Kang, Rosie Bell, Hannes Ausserwöger, Pietro Sormanni, Michele Vendruscolo:
Sequence-based prediction of pH-dependent protein solubility using CamSol. - Hsin-Hua Chen, Chun-Wei Hsueh, Chia-Hwa Lee, Ting-Yi Hao, Tzu-Ying Tu, Lan-Yun Chang, Jih-Chin Lee, Chun-Yu Lin:
SWEET: a single-sample network inference method for deciphering individual features in disease. - Marianna Milano, Mario Cannataro:
Network models in bioinformatics: modeling and analysis for complex diseases. - Sagar Gupta, Ravi Shankar:
miWords: transformer-based composite deep learning for highly accurate discovery of pre-miRNA regions across plant genomes. - Yansen Su, Rongxin Lin, Jing Wang, Dayu Tan, Chun-Hou Zheng:
Denoising adaptive deep clustering with self-attention mechanism on single-cell sequencing data. - Ziyu Tao, Shixiang Wang, Chenxu Wu, Tao Wu, Xiangyu Zhao, Wei Ning, Guangshuai Wang, Jinyu Wang, Jing Chen, Kaixuan Diao, Fuxiang Chen, Xue-Song Liu:
The repertoire of copy number alteration signatures in human cancer. - Sosie Yorki, Terrance Shea, Christina Cuomo, Bruce J. Walker, Regina C. Larocque, Abigail L. Manson, Ashlee M. Earl, Colin J. Worby:
Comparison of long- and short-read metagenomic assembly for low-abundance species and resistance genes. - Diane Duroux, Kristel Van Steen:
netANOVA: novel graph clustering technique with significance assessment via hierarchical ANOVA. - Yiwen Wang, Kim-Anh Lê Cao:
PLSDA-batch: a multivariate framework to correct for batch effects in microbiome data. - Xiangwen Ji, Edwin Wang, Qinghua Cui:
Deciphering gene contributions and etiologies of somatic mutational signatures of cancer. - Jiayuan Zhong, Dandan Ding, Juntan Liu, Rui Liu, Pei Chen:
SPNE: sample-perturbed network entropy for revealing critical states of complex biological systems. - Joongho Lee, Minsoo Kim, Keunsoo Kang, Chul-Su Yang, Seokhyun Yoon:
Hierarchical cell-type identifier accurately distinguishes immune-cell subtypes enabling precise profiling of tissue microenvironment with single-cell RNA-sequencing. - Han Sun, Yue Wang, Zhen Xiao, Xiaoyun Huang, Haodong Wang, Tingting He, Xingpeng Jiang:
multiMiAT: an optimal microbiome-based association test for multicategory phenotypes. - Correction to: 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.
- Wei Zhang, Jia Ju, Yong Zhou, Teng Xiong, Mengyao Wang, Chaohui Li, Shixin Lu, Zefeng Lu, Liya Lin, Xiao Liu, Shuaicheng Li:
IMperm: a fast and comprehensive IMmune Paired-End Reads Merger for sequencing data. - Tao Deng, Siyu Chen, Ying Zhang, Yuanbin Xu, Da Feng, Hao Wu, Xiaobo Sun:
A cofunctional grouping-based approach for non-redundant feature gene selection in unannotated single-cell RNA-seq analysis. - Li Liu, Kaiyuan Han, Huimin Sun, Lu Han, Dong Gao, Qilemuge Xi, Lirong Zhang, Hao Lin:
A comprehensive review of bioinformatics tools for chromatin loop calling. - Kahn Rhrissorrakrai, Filippo Utro, Chaya Levovitz, Laxmi Parida:
Lesion Shedding Model: unraveling site-specific contributions to ctDNA. - Saisai Tian, Jinbo Zhang, Shunling Yuan, Qun Wang, Chao Lv, Jinxing Wang, Jiansong Fang, Lu Fu, Jian Yang, Xianpeng Zu, Jing Zhao, Weidong Zhang:
Exploring pharmacological active ingredients of traditional Chinese medicine by pharmacotranscriptomic map in ITCM. - Kai-Yue Ji, Chong Liu, Zhao-Qian Liu, Yafeng Deng, Tingjun Hou, Dong-Sheng Cao:
Comprehensive assessment of nine target prediction web services: which should we choose for target fishing? - Dario Romagnoli, Agostina Nardone, Francesca Galardi, Marta Paoli, Francesca De Luca, Chiara Biagioni, Gian Marco Franceschini, Marta Pestrin, Giuseppina Sanna, Erica Moretti, Francesca Demichelis, Ilenia Migliaccio, Laura Biganzoli, Luca Malorni, Matteo Benelli:
MIMESIS: minimal DNA-methylation signatures to quantify and classify tumor signals in tissue and cell-free DNA samples. - Haiwei Zhou, Wenxi Tan, Shao-Ping Shi:
DeepGpgs: a novel deep learning framework for predicting arginine methylation sites combined with Gaussian prior and gated self-attention mechanism. - Yu'e Huang, Shunheng Zhou, Haizhou Liu, Xu Zhou, Mengqin Yuan, Fei Hou, Sina Chen, Jiahao Chen, Lihong Wang, Wei Jiang:
DRdriver: identifying drug resistance driver genes using individual-specific gene regulatory network. - Yuansong Zeng, Rui Yin, Mai Luo, Jianing Chen, Zixiang Pan, Yutong Lu, Weijiang Yu, Yuedong Yang:
Identifying spatial domain by adapting transcriptomics with histology through contrastive learning. - Juhua Pu, Bingchen Wang, Xingwu Liu, Lingxi Chen, Shuai Cheng Li:
SMURF: embedding single-cell RNA-seq data with matrix factorization preserving self-consistency. - Zhenmiao Zhang, Chao Yang, Werner Pieter Veldsman, Xiaodong Fang, Lu Zhang:
Benchmarking genome assembly methods on metagenomic sequencing data. - Jingxuan Bao, Changgee Chang, Qiyiwen Zhang, Andrew J. Saykin, Li Shen, Qi Long:
Integrative analysis of multi-omics and imaging data with incorporation of biological information via structural Bayesian factor analysis. - Correction to: kTWAS: integrating kernel machine with transcriptome-wide association studies improves statistical power and reveals novel genes.
- Yutong Yu, Pengju Ding, Hongli Gao, Guozhu Liu, Fa Zhang, Bin Yu:
Cooperation of local features and global representations by a dual-branch network for transcription factor binding sites prediction. - Weizhong Zhao, Xueling Yuan, Xianjun Shen, Xingpeng Jiang, Chuan Shi, Tingting He, Xiaohua Hu:
Improving drug-drug interactions prediction with interpretability via meta-path-based information fusion. - Mang Liang, Sheng Cao, Tianyu Deng, Lili Du, Keanning Li, Bingxing An, Yueying Du, Lingyang Xu, Lupei Zhang, Xue Gao, Junya Li, Peng Guo, Huijiang Gao:
MAK: a machine learning framework improved genomic prediction via multi-target ensemble regressor chains and automatic selection of assistant traits. - Yuan-Qin Huang, Ping Sun, Yi Chen, Huanxiang Liu, Ge-Fei Hao, Bao-An Song:
Bioinformatics toolbox for exploring target mutation-induced drug resistance. - Qiushi Cao, Cheng Ge, Xuejie Wang, Peta J. Harvey, Zixuan Zhang, Yuan Ma, Xianghong Wang, Xinying Jia, Mehdi Mobli, David J. Craik, Tao Jiang, Jinbo Yang, Zhiqiang Wei, Yan Wang, Shan Chang, Rilei Yu:
Designing antimicrobial peptides using deep learning and molecular dynamic simulations. - Zhou Yao, Wenjing Zhang, Peng Song, Yuxue Hu, Jianxiao Liu:
DeepFormer: a hybrid network based on convolutional neural network and flow-attention mechanism for identifying the function of DNA sequences. - Jie Hao, Jiawei Zou, Jiaqiang Zhang, Ke Chen, Duojiao Wu, Wei Cao, Guoguo Shang, Jean Yee Hwa Yang, KongFatt Wong-Lin, Hourong Sun, Zhen Zhang, Xiangdong Wang, Wantao Chen, Xin Zou:
scSTAR reveals hidden heterogeneity with a real-virtual cell pair structure across conditions in single-cell RNA sequencing data. - Lianming Du, Chaoyue Geng, Qiang-Lin Zeng, Ting Huang, Jie Tang, Yiwen Chu, Kelei Zhao:
Dockey: a modern integrated tool for large-scale molecular docking and virtual screening. - Xiang Liu, Huitao Feng, Zhi Lü, Kelin Xia:
Persistent Tor-algebra for protein-protein interaction analysis. - Peike Wu, Renliang Sun, Aamir Fahira, Yongzhou Chen, Huiting Jiangzhou, Ke Wang, Qiangzhen Yang, Yang Dai, Dun Pan, Yongyong Shi, Zhuo Wang:
DROEG: a method for cancer drug response prediction based on omics and essential genes integration. - Harsh Sharma, Trishna Pani, Ujjaini Dasgupta, Jyotsna Batra, Ravi Datta Sharma:
Prediction of transcript structure and concentration using RNA-Seq data. - Yongtian Wang, Xinmeng Liu, Yewei Shen, Xuerui Song, Tao Wang, Xuequn Shang, Jiajie Peng:
Collaborative deep learning improves disease-related circRNA prediction based on multi-source functional information. - Tingting Zhao, Guangyu Zhu, Harsh Vardhan Dubey, Patrick Flaherty:
Identification of significant gene expression changes in multiple perturbation experiments using knockoffs. - Connor H. Knight, Faraz Khan, Ankit Patel, Upkar S. Gill, Jessica Okosun, Jun Wang:
IBRAP: integrated benchmarking single-cell RNA-sequencing analytical pipeline. - Xinru Ruan, Changzhi Jiang, Peixuan Lin, Yuan Lin, Juan Liu, Shaohui Huang, Xiangrong Liu:
MSGCL: inferring miRNA-disease associations based on multi-view self-supervised graph structure contrastive learning. - Hui Liu, Ye Jin, Hanjing Ding:
MDBuilder: a PyMOL plugin for the preparation of molecular dynamics simulations. - Bin Liu, Jin Wang, Kaiwei Sun, Grigorios Tsoumakas:
Fine-grained selective similarity integration for drug-target interaction prediction. - Lianhe Zhao, Xiaoning Qi, Yang Chen, Yixuan Qiao, Dechao Bu, Yang Wu, Yufan Luo, Sheng Wang, Rui Zhang, Yi Zhao:
Biological knowledge graph-guided investigation of immune therapy response in cancer with graph neural network. - Correction to: Prediction of transcript structure and concentration using RNA-Seq data.
- Yueh-Hua Tu, Hsueh-Fen Juan, Hsuan-Cheng Huang:
Context-dependent gene regulatory network reveals regulation dynamics and cell trajectories using unspliced transcripts. - Sijia Wu, Qiuping Xue, Mengyuan Yang, Yanfei Wang, Pora Kim, Xiaobo Zhou, Liyu Huang:
Genetic control of RNA editing in neurodegenerative disease. - Rulan Wang, Chia-Ru Chung, Hsien-Da Huang, Tzong-Yi Lee:
Identification of species-specific RNA N6-methyladinosine modification sites from RNA sequences. - Ina Bang, Sang-Mok Lee, Seojoung Park, Joon Young Park, Linh Khanh Nong, Ye Gao, Bernhard O. Palsson, Donghyuk Kim:
Deep-learning optimized DEOCSU suite provides an iterable pipeline for accurate ChIP-exo peak calling. - Xingxin Pan, Zeynep H. Coban Akdemir, Ruixuan Gao, Xiaoqian Jiang, Gloria M. Sheynkman, Erxi Wu, Jason H. Huang, Nidhi Sahni, S. Stephen Yi:
AD-Syn-Net: systematic identification of Alzheimer's disease-associated mutation and co-mutation vulnerabilities via deep learning. - Tao Tang, Xiaocai Zhang, Yuansheng Liu, Hui Peng, Binshuang Zheng, Yanlin Yin, Xiangxiang Zeng:
Machine learning on protein-protein interaction prediction: models, challenges and trends. - Yamao Chen, Shengyu Zhou, Ming Li, Fangqing Zhao, Ji Qi:
STEEL enables high-resolution delineation of spatiotemporal transcriptomic data. - Chenxu Pan, René Rahn, David Heller, Knut Reinert:
Linear: a framework to enable existing software to resolve structural variants in long reads with flexible and efficient alignment-free statistical models. - A Samet Özdilek, Ahmet Atakan, Gökhan Özsari, Aybar C. Acar, M. Volkan Atalay, Tunca Dogan, Ahmet Süreyya Rifaioglu:
ProFAB - open protein functional annotation benchmark. - Zimo Huang, Jun Wang, Xudong Lu, Azlan Mohd Zain, Guoxian Yu:
scGGAN: single-cell RNA-seq imputation by graph-based generative adversarial network. - Mingzhu Liu, Jian Zhou, Qilemuge Xi, Yuchao Liang, Haicheng Li, Pengfei Liang, Yuting Guo, Ming Liu, Temuqile Temuqile, Lei Yang, Yongchun Zuo:
A computational framework of routine test data for the cost-effective chronic disease prediction. - María Isabel Alcoriza-Balaguer, Juan Carlos García-Cañaveras, Marta Benet, Oscar Juan-Vidal, Agustín Lahoz:
FAMetA: a mass isotopologue-based tool for the comprehensive analysis of fatty acid metabolism. - Xiaoti Jia, Pei Zhao, Fuyi Li, Zhaohui Qin, Haoran Ren, Junzhou Li, Chunbo Miao, Quanzhi Zhao, Tatsuya Akutsu, Gensheng Dou, Zhen Chen, Jiangning Song:
ResNetKhib: a novel cell type-specific tool for predicting lysine 2-hydroxyisobutylation sites via transfer learning. - Qiao Ning, Yaomiao Zhao, Jun Gao, Chen Chen, Xiang Li, Tingting Li, Minghao Yin:
AMHMDA: attention aware multi-view similarity networks and hypergraph learning for miRNA-disease associations identification. - Zi-Jun Yan, Chu-Ting Yu, Lei Chen, Hong-Yang Wang:
Development of a TMErisk model based on immune infiltration in tumour microenvironment to predict prognosis of immune checkpoint inhibitor treatment in hepatocellular carcinoma. - Bertrand Jern Han Wong, Weijia Kong, Hui Peng, Wilson Wen Bin Goh:
PROSE: phenotype-specific network signatures from individual proteomic samples. - Yanpeng Zhao, Jingjing Wang, Fubin Chang, Weikang Gong, Yang Liu, Chunhua Li:
Identification of metal ion-binding sites in RNA structures using deep learning method. - Fei Zhu, Lei Deng, Yuhao Dai, Guangyu Zhang, Fanwang Meng, Cheng Luo, Guang Hu, Zhongjie Liang:
PPICT: an integrated deep neural network for predicting inter-protein PTM cross-talk. - Chirag Parsania, Ruiwen Chen, Pooja Sethiya, Zhengqiang Miao, Liguo Dong, Koon Ho Wong:
FungiExpresZ: an intuitive package for fungal gene expression data analysis, visualization and discovery. - Victoria A Kobets, Sergey V. Ulianov, Aleksandra A. Galitsyna, Semen A. Doronin, Elena A. Mikhaleva, Mikhail S. Gelfand, Yuri Y. Shevelyov, Sergey V. Razin, Ekaterina E. Khrameeva:
HiConfidence: a novel approach uncovering the biological signal in Hi-C data affected by technical biases. - Le Yuan, Hongzhong Lu, Feiran Li, Jens Nielsen, Eduard J. Kerkhoven:
HGTphyloDetect: facilitating the identification and phylogenetic analysis of horizontal gene transfer. - Tengwei Zhong, Wenqing Wang, Houyan Liu, Maolin Zeng, Xinyu Zhao, Zhiyun Guo:
eccDNA Atlas: a comprehensive resource of eccDNA catalog. - Andrew Nakamura, Hanze Meng, Minglei Zhao, Fengbin Wang, Jie Hou, Renzhi Cao, Dong Si:
Fast and automated protein-DNA/RNA macromolecular complex modeling from cryo-EM maps. - Zhiwei Cheng, Leijie Li, Yuening Zhang, Yongyong Ren, Jianlei Gu, Xinbo Wang, Hongyu Zhao, Hui Lu:
HBV-infected hepatocellular carcinoma can be robustly classified into three clinically relevant subgroups by a novel analytical protocol. - Xiao Wang, Zhao-Yuan Ding, Rong Wang, Xi Lin:
Deepro-Glu: combination of convolutional neural network and Bi-LSTM models using ProtBert and handcrafted features to identify lysine glutarylation sites. - Binjie Guo, Hanyu Zheng, Haohan Jiang, Xiaodan Li, Naiyu Guan, Yanming Zuo, Yicheng Zhang, Hengfu Yang, Xuhua Wang:
Enhanced compound-protein binding affinity prediction by representing protein multimodal information via a coevolutionary strategy. - Yanping Zhao, Kui Wang, Gang Hu:
DIST: spatial transcriptomics enhancement using deep learning. - Shukai Gu, Chao Shen, Jiahui Yu, Hong Zhao, Huanxiang Liu, Liwei Liu, Rong Sheng, Lei Xu, Zhe Wang, Tingjun Hou, Yu Kang:
Can molecular dynamics simulations improve predictions of protein-ligand binding affinity with machine learning? - Ran Zhang, Zhanjie Wang, Xuezhi Wang, Zhen Meng, Wenjuan Cui:
MHTAN-DTI: Metapath-based hierarchical transformer and attention network for drug-target interaction prediction. - Minwoo Pak, Sangseon Lee, Inyoung Sung, Bonil Koo, Sun Kim:
Improved drug response prediction by drug target data integration via network-based profiling. - Yuqi Sheng, Jiashuo Wu, Xiangmei Li, Jiayue Qiu, Ji Li, Qinyu Ge, Liang Cheng, Junwei Han:
iATMEcell: identification of abnormal tumor microenvironment cells to predict the clinical outcomes in cancer based on cell-cell crosstalk network. - Fei-Liao Lai, Feng Gao:
Auto-Kla: a novel web server to discriminate lysine lactylation sites using automated machine learning. - Jilong Bian, Xi Zhang, Xiying Zhang, Dali Xu, Guohua Wang:
MCANet: shared-weight-based MultiheadCrossAttention network for drug-target interaction prediction. - Renfei Ma, Shangfu Li, Luca Parisi, Wenshuo Li, Hsien-Da Huang, Tzong-Yi Lee:
Holistic similarity-based prediction of phosphorylation sites for understudied kinases.
Volume 24, Number 3, May 2023
- Federico Gossi, Pushpak Pati, Panagiotis Chouvardas, Adriano Luca Martinelli, Marianna Kruithof-de Julio, Maria Anna Rapsomaniki:
Matching single cells across modalities with contrastive learning and optimal transport. - Correction to: Molecular persistent spectral image (Mol-PSI) representation for machine learning models in drug design.
- Akshay Khanduja, Manish Kumar, Debasisa Mohanty:
ProsmORF-pred: a machine learning-based method for the identification of small ORFs in prokaryotic genomes. - Fengqi Ge, Chunxiang Peng, Xinyue Cui, Yuhao Xia, Guijun Zhang:
Inter-domain distance prediction based on deep learning for domain assembly. - Ryuji Hamamoto, Ken Takasawa, Norio Shinkai, Hidenori Machino, Nobuji Kouno, Ken Asada, Masaaki Komatsu, Syuzo Kaneko:
Analysis of super-enhancer using machine learning and its application to medical biology. - Ruiqiang Lu, Jun Wang, Pengyong Li, Yuquan Li, Shuoyan Tan, Yiting Pan, Huanxiang Liu, Peng Gao, Guotong Xie, Xiaojun Yao:
Improving drug-target affinity prediction via feature fusion and knowledge distillation. - Hongjia Liu, Huamei Li, Amit Sharma, Wenjuan Huang, Duo Pan, Yu Gu, Lu Lin, Xiao Sun, Hongde Liu:
scAnno: a deconvolution strategy-based automatic cell type annotation tool for single-cell RNA-sequencing data sets. - Ke Shen, Ahmad Ud Din, Baivab Sinha, Yi Zhou, Fuliang Qian, Bairong Shen:
Translational informatics for human microbiota: data resources, models and applications. - Zheng Ye, Shaohao Li, Xue Mi, Baoyi Shao, Zhu Dai, Bo Ding, Songwei Feng, Bo Sun, Yang Shen, Zhongdang Xiao:
STMHCpan, an accurate Star-Transformer-based extensible framework for predicting MHC I allele binding peptides. - Hui Zhang, Hongjia Li, Fa Zhang, Ping Zhu:
A strategy combining denoising and cryo-EM single particle analysis. - Feng Zhang, Huiyuan Jiao, Yihao Wang, Chen Yang, Linying Li, Zhiming Wang, Ran Tong, Junmei Zhou, Jianfeng Shen, Lingjie Li:
InferLoop: leveraging single-cell chromatin accessibility for the signal of chromatin loop. - Ge Zhang, Zhijie Gao, Chaokun Yan, Jianlin Wang, Wenjuan Liang, Junwei Luo, Huimin Luo:
KGANSynergy: knowledge graph attention network for drug synergy prediction. - Ying Liao, Yisha Xiang, Mingjie Zheng, Jun Wang:
DeepMiceTL: a deep transfer learning based prediction of mice cardiac conduction diseases using early electrocardiograms. - Mingliang Dou, Jiaqi Ding, Genlang Chen, Junwen Duan, Fei Guo, Jijun Tang:
IK-DDI: a novel framework based on instance position embedding and key external text for DDI extraction. - Niloofar Yousefi, Mehdi Yazdani-Jahromi, Aida Tayebi, Elayaraja Kolanthai, Craig J. Neal, Tanumoy Banerjee, Agnivo Gosai, Ganesh Balasubramanian, Sudipta Seal, Özlem Özmen Garibay:
BindingSite-AugmentedDTA: enabling a next-generation pipeline for interpretable prediction models in drug repurposing. - Joshua Teitz, Jörg Sander, Hassan Sarker, Carlos Fernandez-Patron:
Potential of dissimilarity measure-based computation of protein thermal stability data for determining protein interactions. - Qi Lu, Ruihan Zhang, Hongyuan Zhou, Dongxuan Ni, Weilie Xiao, Jin Li:
MetaHMEI: meta-learning for prediction of few-shot histone modifying enzyme inhibitors. - Xiao Wang, Lijun Han, Rong Wang, Haoran Chen:
DaDL-SChlo: protein subchloroplast localization prediction based on generative adversarial networks and pre-trained protein language model. - Ying-Ying Zhang, De-Min Liang, Pu-Feng Du:
iEssLnc: quantitative estimation of lncRNA gene essentialities with meta-path-guided random walks on the lncRNA-protein interaction network. - Xin Wang, Xin Gao, Guohua Wang, Dan Li:
miProBERT: identification of microRNA promoters based on the pre-trained model BERT. - Hang Zhao, Pin-yuan Dai, Xiao-jin Yu, Jieyu He, Chao Zhao, Li-hong Yin:
Evaluation of graphical models for multi-group metabolomics data. - Pablo Acera Mateos, You Zhou, Kathi Zarnack, Eduardo Eyras:
Concepts and methods for transcriptome-wide prediction of chemical messenger RNA modifications with machine learning. - Minghao Jiang, Shiyan Zhang, Hongxin Yin, Zhiyi Zhuo, Guoyu Meng:
A comprehensive benchmarking of differential splicing tools for RNA-seq analysis at the event level. - Tao Wu, Yao Dai, Yue Xu, Jie Zheng, Shuting Chen, Yinuo Zhang, Peng Tian, Xiaoqi Zheng, Haiyun Wang:
ExosomePurity: tumour purity deconvolution in serum exosomes based on miRNA signatures. - Correction to: Quantum computing algorithms: getting closer to critical problems in computational biology.
- Zhenjiao Du, Xingjian Ding, Yixiang Xu, Yonghui Li:
UniDL4BioPep: a universal deep learning architecture for binary classification in peptide bioactivity. - Zengrui Guan, Zhenran Jiang:
Transformer-based anti-noise models for CRISPR-Cas9 off-target activities prediction. - Jing Jiang, Junlin Xu, Yuansheng Liu, Bosheng Song, Xiulan Guo, Xiangxiang Zeng, Quan Zou:
Dimensionality reduction and visualization of single-cell RNA-seq data with an improved deep variational autoencoder. - Yuting Zhou, Yongquan Jiang, Yan Yang:
AGAT-PPIS: a novel protein-protein interaction site predictor based on augmented graph attention network with initial residual and identity mapping. - Kaixuan Xiao, Yu Wang, Kangning Dong, Shihua Zhang:
SmartGate is a spatial metabolomics tool for resolving tissue structures. - Fatemeh Farhadi, Mohammad Allahbakhsh, Ali Maghsoudi, Nadieh Armin, Haleh Amintoosi:
DiMo: discovery of microRNA motifs using deep learning and motif embedding. - Ajay Subbaroyan, Priyotosh Sil, Olivier C. Martin, Areejit Samal:
Leveraging developmental landscapes for model selection in Boolean gene regulatory networks. - Feng Chen, Yubo Bai, Chunhe Li:
Estimation of non-equilibrium transition rate from gene expression data. - Jun Wang, Xi Chen, Zhengtian Wu, Maozu Guo, Guoxian Yu:
Cooperative driver pathways discovery by multiplex network embedding. - Xiaoqiang Huang, Jun Zhou, Dongshan Yang, Jifeng Zhang, Xiaofeng Xia, Yuqing Eugene Chen, Jie Xu:
Decoding CRISPR-Cas PAM recognition with UniDesign. - Yijia Li, Jonathan Nguyen, David C. Anastasiu, Edgar A. Arriaga:
CosTaL: an accurate and scalable graph-based clustering algorithm for high-dimensional single-cell data analysis. - Jian Liu, Pingjing Li, Jialiang Sun, Jun Guo:
LPAD: using network construction and label propagation to detect topologically associating domains from Hi-C data. - Junwei Luo, Ting Guan, Guolin Chen, Zhonghua Yu, Haixia Zhai, Chaokun Yan, Huimin Luo:
SLHSD: hybrid scaffolding method based on short and long reads. - Anna Klimovskaia Susmelj, Yani Ren, Yann Vander Meersche, Jean-Christophe Gelly, Tatiana Galochkina:
Poincaré maps for visualization of large protein families. - Yiyou Song, Yue Wang, Xuan Wang, Daiyun Huang, Anh Nguyen, Jia Meng:
Multi-task adaptive pooling enabled synergetic learning of RNA modification across tissue, type and species from low-resolution epitranscriptomes. - Tong Zhang, Shao-Wu Zhang, Ming-Yu Xie, Yan Li:
A novel heterophilic graph diffusion convolutional network for identifying cancer driver genes. - Daoyu Duan, Wen Tang, Runshu Wang, Zhenxing Guo, Hao Feng:
Evaluation of epitranscriptome-wide N6-methyladenosine differential analysis methods. - Hongli Gao, Bin Zhang, Long Liu, Shan Li, Xin Gao, Bin Yu:
A universal framework for single-cell multi-omics data integration with graph convolutional networks. - Yang Li, Zihou Guo, Keqi Wang, Xin Gao, Guohua Wang:
End-to-end interpretable disease-gene association prediction. - Hailin Wei, Tong Han, Taiwen Li, Qiu Wu, Chenfei Wang:
SCREE: a comprehensive pipeline for single-cell multi-modal CRISPR screen data processing and analysis. - Lun Li, Bo Xu, Dongmei Tian, Anke Wang, Junwei Zhu, Cuiping Li, Na Li, Wei Zhao, Leisheng Shi, Yongbiao Xue, Zhang Zhang, Yiming Bao, Wenming Zhao, Shuhui Song:
McAN: a novel computational algorithm and platform for constructing and visualizing haplotype networks. - Xin-Fei Wang, Changqing Yu, Zhu-Hong You, Li-Ping Li, Wenzhun Huang, Zhong-Hao Ren, Yue-Chao Li, Weixiao Meng:
A feature extraction method based on noise reduction for circRNA-miRNA interaction prediction combining multi-structure features in the association networks. - Jiaqi Luo, Xueying Wang, Yiping Zou, Lingxi Chen, Wei Liu, Wei Zhang, Shuai Cheng Li:
Quantitative annotations of T-Cell repertoire specificity. - Kechi Fang, Chuan Li, Jing Wang:
An automatic immunofluorescence pattern classification framework for HEp-2 image based on supervised learning. - Yuanchun Zhao, Xingqi Chen, Jiajia Chen, Xin Qi:
Decoding Connectivity Map-based drug repurposing for oncotherapy. - Weihe Dong, Qiang Yang, Jian Wang, Long Xu, Xiaokun Li, Gongning Luo, Xin Gao:
Multi-modality attribute learning-based method for drug-protein interaction prediction based on deep neural network. - Qikai Niu, Hongtao Li, Lin Tong, Sihong Liu, Wenjing Zong, Siqi Zhang, Siwei Tian, Jingai Wang, Jun Liu, Bing Li, Zhong Wang, Huamin Zhang:
TCMFP: a novel herbal formula prediction method based on network target's score integrated with semi-supervised learning genetic algorithms. - Yongfan Ming, Wenkang Wang, Rui Yin, Min Zeng, Li Tang, Shizhe Tang, Min Li:
A review of enzyme design in catalytic stability by artificial intelligence. - An-phi Nguyen, Stefania Vasilaki, María Rodríguez Martínez:
FLAN: feature-wise latent additive neural models for biological applications. - Martin Bishop:
Editorial. - Sikang Chen, Jian Gao, Jiexuan Chen, Yufeng Xie, Zheyuan Shen, Lei Xu, Jinxin Che, Jian Wu, Xiaowu Dong:
ClusterX: a novel representation learning-based deep clustering framework for accurate visual inspection in virtual screening. - Mengsha Tong, Yuxiang Lin, Wenxian Yang, Jinsheng Song, Zheyang Zhang, Jiajing Xie, Jingyi Tian, Shijie Luo, Chenyu Liang, Jialiang Huang, Rongshan Yu:
Prioritizing prognostic-associated subpopulations and individualized recurrence risk signatures from single-cell transcriptomes of colorectal cancer. - Guangshuai Wang, Tao Wu, Wei Ning, Kaixuan Diao, Xiaoqin Sun, Jinyu Wang, Chenxu Wu, Jing Chen, Dongliang Xu, Xue-Song Liu:
TLimmuno2: predicting MHC class II antigen immunogenicity through transfer learning. - Camilo Rebolledo, Juan Pablo Silva, Nicolás Saavedra, Vinicius Maracaja-Coutinho:
Computational approaches for circRNAs prediction and in silico characterization. - Mohammad Rizwan Alam, Kyung Jin Seo, Jamshid Abdul-Ghafar, Kwangil Yim, Sung Hak Lee, Hyun-Jong Jang, Chan Kwon Jung, Yosep Chong:
Recent application of artificial intelligence on histopathologic image-based prediction of gene mutation in solid cancers. - Koushik Mallick, Sikim Chakraborty, Saurav Mallik, Sanghamitra Bandyopadhyay:
A scalable unsupervised learning of scRNAseq data detects rare cells through integration of structure-preserving embedding, clustering and outlier detection. - Long-Hao Jia, Yingjian Wu, Yanqi Dong, Jingchao Chen, Wei-Hua Chen, Xing-Ming Zhao:
A survey on computational strategies for genome-resolved gut metagenomics. - Meng Wang, Lukasz A. Kurgan, Min Li:
A comprehensive assessment and comparison of tools for HLA class I peptide-binding prediction. - Ruyi Chen, Fuyi Li, Xudong Guo, Yue Bi, Chen Li, Shirui Pan, Lachlan J. M. Coin, Jiangning Song:
ATTIC is an integrated approach for predicting A-to-I RNA editing sites in three species. - Kai-Li Chang, Jia-Hong Chen, Tzu-Chieh Lin, Jun-Yi Leu, Cheng-Fu Kao, Jin Yung Wong, Huai-Kuang Tsai:
Short human eccDNAs are predictable from sequences. - Wei Xu, Teng Wang, Nan Wang, Haohong Zhang, Yuguo Zha, Lei Ji, Yuwen Chu, Kang Ning:
Artificial intelligence-enabled microbiome-based diagnosis models for a broad spectrum of cancer types. - Yan Wang, Chenxu Xuan, Hanwen Wu, Bai Zhang, Tao Ding, Jie Gao:
P-CSN: single-cell RNA sequencing data analysis by partial cell-specific network. - Teng Liu, Zhao-Yu Fang, Xin Li, Li-Ning Zhang, Dong-Sheng Cao, Ming-Zhu Yin:
Graph deep learning enabled spatial domains identification for spatial transcriptomics. - Yi-Gang Chen, Yang-Chi-Dung Lin, Yijun Luo, Xiao-Xuan Cai, Peng Qiu, Shi-Dong Cui, Zhe Wang, Hsi-Yuan Huang, Hsien-Da Huang:
Quantitative model for genome-wide cyclic AMP receptor protein binding site identification and characteristic analysis. - Hongning Zhang, Mingkun Lu, Gaole Lin, Lingyan Zheng, Wei Zhang, Zhijian Xu, Feng Zhu:
SoCube: an innovative end-to-end doublet detection algorithm for analyzing scRNA-seq data. - Bing Li, Tian Wang, Min Qian, Shuang Wang:
MKMR: a multi-kernel machine regression model to predict health outcomes using human microbiome data. - Yushan Qiu, Chang Yan, Pu Zhao, Quan Zou:
SSNMDI: a novel joint learning model of semi-supervised non-negative matrix factorization and data imputation for clustering of single-cell RNA-seq data. - Sumit Mukherjee, Matan Drory Retwitzer, Sara M. Hubbell, Michelle M. Meyer, Danny Barash:
A computational approach for the identification of distant homologs of bacterial riboswitches based on inverse RNA folding. - Ke Xu, Chinwang Cheong, Werner Pieter Veldsman, Aiping Lyu, William K. Cheung, Lu Zhang:
Accurate and interpretable gene expression imputation on scRNA-seq data using IGSimpute. - Yan Liu, Guo Wei, Chen Li, Long-Chen Shen, Robin B. Gasser, Jiangning Song, Dijun Chen, Dong-Jun Yu:
TripletCell: a deep metric learning framework for accurate annotation of cell types at the single-cell level. - Qianmu Yuan, Junjie Xie, Jiancong Xie, Huiying Zhao, Yuedong Yang:
Fast and accurate protein function prediction from sequence through pretrained language model and homology-based label diffusion. - Yue Cao, Shila Ghazanfar, Pengyi Yang, Jean Y. H. Yang:
Benchmarking of analytical combinations for COVID-19 outcome prediction using single-cell RNA sequencing data. - Peilin Jia, Ruifeng Hu, Zhongming Zhao:
Benchmark of embedding-based methods for accurate and transferable prediction of drug response. - Zhenhua Yu, Furui Liu, Fangyuan Shi, Fang Du:
rcCAE: a convolutional autoencoder method for detecting intra-tumor heterogeneity and single-cell copy number alterations. - Prem Singh Bist, Hilal Tayara, Kil To Chong:
Sars-escape network for escape prediction of SARS-COV-2. - Jiashu Liu, Cui-Xiang Lin, Xiaoqi Zhang, Zongxuan Li, Wenkui Huang, Jin Liu, Yuanfang Guan, Hong-Dong Li:
Computational approaches for detecting disease-associated alternative splicing events. - Bruna Moreira da Silva, David B. Ascher, Douglas E. V. Pires:
epitope1D: accurate taxonomy-aware B-cell linear epitope prediction. - Marek Justyna, Maciej Antczak, Marta Szachniuk:
Machine learning for RNA 2D structure prediction benchmarked on experimental data. - Manuel Tognon, Rosalba Giugno, Luca Pinello:
A survey on algorithms to characterize transcription factor binding sites. - Fatima Noor, Muhammad Asif, Usman Ali Ashfaq, Muhammad Qasim, Muhammad Tahir Ul Qamar:
Machine learning for synergistic network pharmacology: a comprehensive overview. - Wei Liu, Yu Yang, Xu Lu, Xiangzheng Fu, Ruiqing Sun, Li Yang, Li Peng:
NSRGRN: a network structure refinement method for gene regulatory network inference. - Zeinab Sherkatghanad, Moloud Abdar, Jérémy Charlier, Vladimir Makarenkov:
Using traditional machine learning and deep learning methods for on- and off-target prediction in CRISPR/Cas9: a review.
Volume 24, Number 4, July 2023
- Mandy Ibéné, Audrey Legendre, Guillaume Postic, Eric Angel, Fariza Tahi:
C-RCPred: a multi-objective algorithm for interactive secondary structure prediction of RNA complexes integrating user knowledge and SHAPE data. - Juntao Chen, Jiannan Chao, Huan Liu, Fenglong Yang, Quan Zou, Furong Tang:
WMSA 2: a multiple DNA/RNA sequence alignment tool implemented with accurate progressive mode and a fast win-win mode combining the center star and progressive strategies. - Kevin E. Wu, James Y. Zou, Howard Chang:
Machine learning modeling of RNA structures: methods, challenges and future perspectives. - Rongtao Zheng, Zhijian Huang, Lei Deng:
Large-scale predicting protein functions through heterogeneous feature fusion. - Taha ValizadehAslani, Yiwen Shi, Ping Ren, Jing Wang, Yi Zhang, Meng Hu, Liang Zhao, Hualou Liang:
PharmBERT: a domain-specific BERT model for drug labels. - Yu Zhao, Xiaona Su, Weitong Zhang, Sijie Mai, Zhimeng Xu, Chenchen Qin, Rongshan Yu, Bing He, Jianhua Yao:
SC-AIR-BERT: a pre-trained single-cell model for predicting the antigen-binding specificity of the adaptive immune receptor. - Guoxing Cai, Wenyi Zhao, Zhan Zhou, Xun Gu:
MATTE: a pipeline of transcriptome module alignment for anti-noise phenotype-gene-related analysis. - Anil Prakash, Moinak Banerjee:
An interpretable block-attention network for identifying regulatory feature interactions. - Ke Yan, Jiawei Feng, Jing Huang, Hao Wu:
iDRPro-SC: identifying DNA-binding proteins and RNA-binding proteins based on subfunction classifiers. - Anja Conev, Maurício Menegatti Rigo, Didier Devaurs, André Faustino Fonseca, Hussain Kalavadwala, Martiela Vaz de Freitas, Cecilia Clementi, Geancarlo Zanatta, Dinler Amaral Antunes, Lydia E. Kavraki:
EnGens: a computational framework for generation and analysis of representative protein conformational ensembles. - Muhammad Toseef, Olutomilayo Olayemi Petinrin, Fuzhou Wang, Saifur Rahaman, Zhe Liu, Xiangtao Li, Ka-Chun Wong:
Deep transfer learning for clinical decision-making based on high-throughput data: comprehensive survey with benchmark results. - Weijia Kong, Bertrand Jern Han Wong, Harvard Wai Hann Hui, Kai Peng Lim, Yulan Wang, Limsoon Wong, Wilson Wen Bin Goh:
ProJect: a powerful mixed-model missing value imputation method. - Gaofei Jiang, Jiaxuan Zhang, Yaozhong Zhang, Xinrun Yang, Tingting Li, Ningqi Wang, Xingjian Chen, Fang-Jie Zhao, Zhong Wei, Yangchun Xu, Qirong Shen, Wei Xue:
DCiPatho: deep cross-fusion networks for genome scale identification of pathogens. - Pengju Ding, Yifei Wang, Xinyu Zhang, Xin Gao, Guozhu Liu, Bin Yu:
DeepSTF: predicting transcription factor binding sites by interpretable deep neural networks combining sequence and shape. - Qiang He, Wei Qiao, Hui Fang, Yang Bao:
Improving the identification of miRNA-disease associations with multi-task learning on gene-disease networks. - Marian Gimeno, Katyna Sada del Real, Angel Rubio:
Precision oncology: a review to assess interpretability in several explainable methods. - Chen-Di Han, Chun-Chun Wang, Li Huang, Xing Chen:
MCFF-MTDDI: multi-channel feature fusion for multi-typed drug-drug interaction prediction. - Juntao Deng, Xiao Zhou, Pengyan Zhang, Weibin Cheng, Min Liu, Junzhang Tian:
IEPAPI: a method for immune epitope prediction by incorporating antigen presentation and immunogenicity. - Yongkang Zhan, Jifeng Guo, C. L. Philip Chen, Xian-Bing Meng:
iBT-Net: an incremental broad transformer network for cancer drug response prediction. - Dayu Hu, Ke Liang, Sihang Zhou, Wenxuan Tu, Meng Liu, Xinwang Liu:
scDFC: A deep fusion clustering method for single-cell RNA-seq data. - Shuangge Ma:
Editorial. - Tao Bai, Ke Yan, Bin Liu:
DAmiRLocGNet: miRNA subcellular localization prediction by combining miRNA-disease associations and graph convolutional networks. - Meiyu Duan, Yueying Wang, Dong Zhao, Hongmei Liu, Gongyou Zhang, Kewei Li, Haotian Zhang, Lan Huang, Ruochi Zhang, Fengfeng Zhou:
Orchestrating information across tissues via a novel multitask GAT framework to improve quantitative gene regulation relation modeling for survival analysis. - Maohua Yang, Zonghua Bo, Tao Xu, Bo Xu, Dongdong Wang, Hang Zheng:
Uni-GBSA: an open-source and web-based automatic workflow to perform MM/GB(PB)SA calculations for virtual screening. - Yanglan Gan, Yuhan Chen, Guangwei Xu, Wenjing Guo, Guobing Zou:
Deep enhanced constraint clustering based on contrastive learning for scRNA-seq data. - Boshen Wang, Xue Lei, Wei Tian, Alan Perez-Rathke, Yan-Yuan Tseng, Jie Liang:
Structure-based pathogenicity relationship identifier for predicting effects of single missense variants and discovery of higher-order cancer susceptibility clusters of mutations. - Xuan Lin, Lichang Dai, Yafang Zhou, Zu-Guo Yu, Wen Zhang, Jian-Yu Shi, Dong-Sheng Cao, Li Zeng, Haowen Chen, Bosheng Song, Philip S. Yu, Xiangxiang Zeng:
Comprehensive evaluation of deep and graph learning on drug-drug interactions prediction. - Xiaoqing Peng, Yiming Li, Mengxi Zou, Xiangyan Kong, Yu Sheng:
CATAD: exploring topologically associating domains from an insight of core-attachment structure. - Hongjun Chen, Yekai Zhou, Yongjing Liu, Peijing Zhang, Ming Chen:
Network integration and protein structural binding analysis of neurodegeneration-related interactome. - Zhongyu Wang, Zhaohong Deng, Wei Zhang, Qiongdan Lou, Kup-Sze Choi, Zhisheng Wei, Lei Wang, Jing Wu:
MMSMAPlus: a multi-view multi-scale multi-attention embedding model for protein function prediction. - Qingchun Liu, Kai Song:
ProgCAE: a deep learning-based method that integrates multi-omics data to predict cancer subtypes. - Kai Li, Ping Zhang, Zilin Wang, Wei Shen, Weicheng Sun, Jinsheng Xu, Zi Wen, Li Li:
iEnhance: a multi-scale spatial projection encoding network for enhancing chromatin interaction data resolution. - Natalia A Szulc, Zuzanna Mackiewicz, Janusz M. Bujnicki, Filip Stefaniak:
Structural interaction fingerprints and machine learning for predicting and explaining binding of small molecule ligands to RNA. - Théo Falgarone, Etienne Villain, François D. Richard, Zarifa Osmanli, Andrey V. Kajava:
Census of exposed aggregation-prone regions in proteomes. - Qi Yang, Zhaochun Xu, Wenyang Zhou, Pingping Wang, Qinghua Jiang, Liran Juan:
An interpretable single-cell RNA sequencing data clustering method based on latent Dirichlet allocation. - Bo Chen, Ziwei Xie, Jiezhong Qiu, Zhaofeng Ye, Jinbo Xu, Jie Tang:
Improved the heterodimer protein complex prediction with protein language models. - José Luis Ruiz, Susanne Reimering, Juan David Escobar-Prieto, Nicolas M. B. Brancucci, Diego F. Echeverry, Abdirahman I Abdi, Matthias Marti, Elena Gómez-Díaz, Thomas D. Otto:
From contigs towards chromosomes: automatic improvement of long read assemblies (ILRA). - Shuang Yang, Weikang Gong, Tong Zhou, Xiaohan Sun, Lei Chen, Wenxue Zhou, Chunhua Li:
emPDBA: protein-DNA binding affinity prediction by combining features from binding partners and interface learned with ensemble regression model. - Han Li, Xuan He, Lawrence Kurowski, Ruotian Zhang, Dan Zhao, Jianyang Zeng:
Improving comparative analyses of Hi-C data via contrastive self-supervised learning. - Göknur Giner, Saima Ikram, Marco J. Herold, Anthony T. Papenfuss:
A systematic review of computational methods for designing efficient guides for CRISPR DNA base editor systems. - Siqi Gao, Hanwen Zhu, Kangwen Cai, Leiqin Liu, Zhiqiang Zhang, Yi Ding, Yaochen Xu, Xiaoqi Zheng, Jiantao Shi:
TRAmHap: accurate prediction of transcriptional activity from DNA methylation haplotypes in bisulfite-sequencing data. - Shudong Wang, Tiyao Liu, Chuanru Ren, Wenhao Wu, Zhiyuan Zhao, Shanchen Pang, Yuanyuan Zhang:
Predicting potential small molecule-miRNA associations utilizing truncated schatten p-norm. - Song Zhai, Bin Guo, Baolin Wu, Devan V. Mehrotra, Judong Shen:
Integrating multiple traits for improving polygenic risk prediction in disease and pharmacogenomics GWAS. - Ze-Ying Feng, Xue-Hong Wu, Jun-Long Ma, Min Li, Ge-Fei He, Dong-Sheng Cao, Guo-Ping Yang:
DKADE: a novel framework based on deep learning and knowledge graph for identifying adverse drug events and related medications. - Yifan Yang, Haoyuan Liu, Yi Liu, Liyuan Zhou, Xiaoqi Zheng, Rongxian Yue, David L. Mattson, Srividya Kidambi, Mingyu Liang, Pengyuan Liu, Xiaoqing Pan:
E-value: a superior alternative to P-value and its adjustments in DNA methylation studies. - Kerui Peng, Theodore S. Nowicki, Katie Campbell, Mohammad Vahed, Dandan Peng, Yiting Meng, Anish Nagareddy, Yu-Ning Huang, Aaron Karlsberg, Zachary Miller, Jaqueline J. Brito, Brian B. Nadel, Victoria M. Pak, Malak S. Abedalthagafi, Amanda M. Burkhardt, Houda Alachkar, Antoni Ribas, Serghei Mangul:
Rigorous benchmarking of T-cell receptor repertoire profiling methods for cancer RNA sequencing. - Yidong Song, Qianmu Yuan, Sheng Chen, Ken Chen, Yaoqi Zhou, Yuedong Yang:
Fast and accurate protein intrinsic disorder prediction by using a pretrained language model. - Xenia Lainscsek, Leila Taher:
Predicting chromosomal compartments directly from the nucleotide sequence with DNA-DDA. - Jeremie Theddy Darmawan, Jenq-Shiou Leu, Cries Avian, Nanda Rizqia Pradana Ratnasari:
MITNet: a fusion transformer and convolutional neural network architecture approach for T-cell epitope prediction. - Liugen Wang, Yan Wang, Chenxu Xuan, Bai Zhang, Hanwen Wu, Jie Gao:
Predicting potential microbe-disease associations based on multi-source features and deep learning. - Guang-Xing He, Jun Liu, Dong Liu, Guijun Zhang:
GraphGPSM: a global scoring model for protein structure using graph neural networks. - Wei Liu, Ting Tang, Xu Lu, Xiangzheng Fu, Yu Yang, Li Peng:
MPCLCDA: predicting circRNA-disease associations by using automatically selected meta-path and contrastive learning. - Chun He, Xinhai Ye, Yi Yang, Liya Hu, Yuxuan Si, Xianxin Zhao, Longfei Chen, Qi Fang, Ying Wei, Fei Wu, Gongyin Ye:
DeepAlgPro: an interpretable deep neural network model for predicting allergenic proteins. - Yu-Hsin Chen, Kuan-Hao Chao, Jin Yung Wong, Chien-Fu Liu, Jun-Yi Leu, Huai-Kuang Tsai:
A feature extraction free approach for protein interactome inference from co-elution data. - Hui Yu, Kangkang Li, Wenmin Dong, Shuanghong Song, Chen Gao, Jianyu Shi:
Attention-based cross domain graph neural network for prediction of drug-drug interactions. - Mingxiang Zhang, Hongli Gao, Xin Liao, Baoxing Ning, Haiming Gu, Bin Yu:
DBGRU-SE: predicting drug-drug interactions based on double BiGRU and squeeze-and-excitation attention mechanism. - Leqi Tian, Tianwei Yu:
An integrated deep learning framework for the interpretation of untargeted metabolomics data. - Ruoxing Li, Mehmet Altan, Alexandre Reuben, Ruitao Lin, John V. Heymach, Hai Tran, Runzhe Chen, Latasha Little, Shawna Hubert, Jianjun Zhang, Ziyi Li:
A novel statistical method for decontaminating T-cell receptor sequencing data. - Cheng Wang, Chuang Yuan, Yahui Wang, Ranran Chen, Yuying Shi, Tao Zhang, Fuzhong Xue, Gary J. Patti, Leyi Wei, Qingzhen Hou:
MPI-VGAE: protein-metabolite enzymatic reaction link learning by variational graph autoencoders. - Karin Elimelech-Zohar, Yaron Orenstein:
An overview on nucleic-acid G-quadruplex prediction: from rule-based methods to deep neural networks. - Jing Xu, Aidi Zhang, Fang Liu, Liang Chen, Xiujun Zhang:
CIForm as a Transformer-based model for cell-type annotation of large-scale single-cell RNA-seq data. - Junyu Yan, Shuai Li, Ying Zhang, Aimin Hao, Qinping Zhao:
ZetaDesign: an end-to-end deep learning method for protein sequence design and side-chain packing. - Minjian Yang, Hanyu Sun, Xue Liu, Xi Xue, Yafeng Deng, Xiaojian Wang:
CMGN: a conditional molecular generation net to design target-specific molecules with desired properties. - Shaofeng Liao, Yujun Zhang, Yifei Qi, Zhuqing Zhang:
Evaluation of sequence-based predictors for phase-separating protein. - Kengo Sato, Michiaki Hamada:
Recent trends in RNA informatics: a review of machine learning and deep learning for RNA secondary structure prediction and RNA drug discovery. - Ran Liu, Ye-Fan Hu, Jian-Dong Huang, Xiaodan Fan:
A Bayesian approach to estimate MHC-peptide binding threshold. - Yan Zhu, Fuyi Li, Xudong Guo, Xiaoyu Wang, Lachlan J. M. Coin, Geoffrey I. Webb, Jiangning Song, Cangzhi Jia:
TIMER is a Siamese neural network-based framework for identifying both general and species-specific bacterial promoters. - Correction to: DeepFormer: a hybrid network based on convolutional neural network and flow-attention mechanism for identifying the function of DNA sequences.
- Qiaozhen Meng, Fei Guo, Jijun Tang:
Improved structure-related prediction for insufficient homologous proteins using MSA enhancement and pre-trained language model. - Haojie Lu, Shuo Zhang, Zhou Jiang, Ping Zeng:
Leveraging trans-ethnic genetic risk scores to improve association power for complex traits in underrepresented populations. - Jing Xu, Fuyi Li, Chen Li, Xudong Guo, Cornelia B. Landersdorfer, Hsin-Hui Shen, Anton Y. Peleg, Jian Li, Seiya Imoto, Jianhua Yao, Tatsuya Akutsu, Jiangning Song:
iAMPCN: a deep-learning approach for identifying antimicrobial peptides and their functional activities. - Correction: Comparison and benchmark of structural variants detected from long read and long-read assembly.
- Hao Zhu, Tong Liu, Zheng Wang:
scHiMe: predicting single-cell DNA methylation levels based on single-cell Hi-C data. - Chuanchao Zhang, Xinxing Li, Wendong Huang, Lequn Wang, Qianqian Shi:
Spatially aware self-representation learning for tissue structure characterization and spatial functional genes identification. - Sheng Hu Qian, Meng-Wei Shi, Dan-Yang Wang, Justin M. Fear, Lu Chen, Yi-Xuan Tu, Hong-Shan Liu, Yuan Zhang, Shuai-Jie Zhang, Shan-Shan Yu, Brian Oliver, Zhen-Xia Chen:
Integrating massive RNA-seq data to elucidate transcriptome dynamics in Drosophila melanogaster. - Jinhang Wei, Linlin Zhuo, Zhecheng Zhou, Xinze Lian, Xiangzheng Fu, Xiaojun Yao:
GCFMCL: predicting miRNA-drug sensitivity using graph collaborative filtering and multi-view contrastive learning. - Jiadong Lin, Peng Jia, Songbo Wang, Walter A. Kosters, Kai Ye:
Comparison and benchmark of structural variants detected from long read and long-read assembly. - Weiqi Zhai, Xiaodi Huang, Nan Shen, Shanfeng Zhu:
Phen2Disease: a phenotype-driven model for disease and gene prioritization by bidirectional maximum matching semantic similarities.
Volume 24, Number 5, September 2023
- Rui Fan, Xiangwen Ji, Jianwei Li, Qinghua Cui, Chunmei Cui:
Defining the single base importance of human mRNAs and lncRNAs. - Baiqing Li, Ting Ran, Hongming Chen:
3D based generative PROTAC linker design with reinforcement learning. - Emily L. Leventhal, Andrea R. Daamen, Peter E. Lipsky:
Letter to the editor: testing the effectiveness of MyPROSLE in classifying patients with lupus nephritis. - Xujun Zhang, Chao Shen, Tianyue Wang, Yafeng Deng, Yu Kang, Dan Li, Tingjun Hou, Peichen Pan:
ML-PLIC: a web platform for characterizing protein-ligand interactions and developing machine learning-based scoring functions. - Hendrick Gao-Min Lim, Yang C. Fann, Yuan-Chii Gladys Lee:
COWID: an efficient cloud-based genomics workflow for scalable identification of SARS-COV-2. - Tao Cui, Tingting Wang:
A comprehensive assessment of hurdle and zero-inflated models for single cell RNA-sequencing analysis. - He Wang, Yongjian Zang, Ying Kang, Jianwen Zhang, Lei Zhang, Shengli Zhang:
ETLD: an encoder-transformation layer-decoder architecture for protein contact and mutation effects prediction. - Ailin Xie, Ziqiao Zhang, Jihong Guan, Shuigeng Zhou:
Self-supervised learning with chemistry-aware fragmentation for effective molecular property prediction. - Jiayi Liu, Anat Kreimer, Wei Vivian Li:
Differential variability analysis of single-cell gene expression data. - Chengming Zhang, Yiwen Yang, Shijie Tang, Kazuyuki Aihara, Chuanchao Zhang, Luonan Chen:
Contrastively generative self-expression model for single-cell and spatial multimodal data. - Zixiao Wang, Shiyang Liang, Siwei Liu, Zhaohan Meng, Jingjie Wang, Shangsong Liang:
Sequence pre-training-based graph neural network for predicting lncRNA-miRNA associations. - Shangru Jia, Artem Lysenko, Keith A. Boroevich, Alok Sharma, Tatsuhiko Tsunoda:
scDeepInsight: a supervised cell-type identification method for scRNA-seq data with deep learning. - David Martínez-Enguita, Sanjiv K. Dwivedi, Rebecka Jörnsten, Mika Gustafsson:
NCAE: data-driven representations using a deep network-coherent DNA methylation autoencoder identify robust disease and risk factor signatures. - Lejin Tian, Yunxiao Xie, Zhaobin Xie, Jasmine Tian, Weidong Tian:
AtacAnnoR: a reference-based annotation tool for single cell ATAC-seq data. - Shengming Zhou, Yetong Zhou, Tian Liu, Jia Zheng, Cangzhi Jia:
PredLLPS_PSSM: a novel predictor for liquid-liquid protein separation identification based on evolutionary information and a deep neural network. - Ko Abe, Teppei Shimamura:
UNMF: a unified nonnegative matrix factorization for multi-dimensional omics data. - Yuhang Liu, Zixuan Wang, Hao Yuan, Guiquan Zhu, Yongqing Zhang:
HEAP: a task adaptive-based explainable deep learning framework for enhancer activity prediction. - Jinyang Wu, Zhiwei Ning, Yidong Ding, Ying Wang, Qinke Peng, Laiyi Fu:
KGETCDA: an efficient representation learning framework based on knowledge graph encoder from transformer for predicting circRNA-disease associations. - Jingjing Wang, Werner Pieter Veldsman, Xiaodong Fang, Yufen Huang, Xuefeng Xie, Aiping Lyu, Lu Zhang:
Benchmarking multi-platform sequencing technologies for human genome assembly. - Dohun Yi, Jin-Wu Nam, Hyobin Jeong:
Toward the functional interpretation of somatic structural variations: bulk- and single-cell approaches. - Xiaobo Yang, Bei Zhang, Siqi Wang, Ye Lu, Kaixian Chen, Cheng Luo, Aihua Sun, Hao Zhang:
OTTM: an automated classification tool for translational drug discovery from omics data. - Pora Kim, Himansu Kumar, Chengyuan Yang, Ruihan Luo, Jiajia Liu, Xiaobo Zhou:
Systematic investigation of the homology sequences around the human fusion gene breakpoints in pan-cancer - bioinformatics study for a potential link to MMEJ. - Jiali Pang, Bilin Liang, Ruifeng Ding, Qiujuan Yan, Ruiyao Chen, Jie Xu:
A denoised multi-omics integration framework for cancer subtype classification and survival prediction. - Shudong Wang, Chuanru Ren, Yulin Zhang, YunYin Li, Shanchen Pang, Tao Song:
Identifying potential small molecule-miRNA associations via Robust PCA based on γ-norm regularization. - Guiying Wu, Mengmeng Song, Ke Wang, Tianyu Cui, Zicong Jiao, Liyan Ji, Xuan Gao, Jiayin Wang, Tao Liu, Xuefeng Xia, Huan Fang, Yanfang Guan, Xin Yi:
DELFMUT: duplex sequencing-oriented depth estimation model for stable detection of low-frequency mutations. - Juan D. Henao, Michael Lauber, Manuel Azevedo, Anastasiia Grekova, Fabian J. Theis, Markus List, Christoph Ogris, Benjamin Schubert:
Multi-omics regulatory network inference in the presence of missing data. - Yuchi Qiu, Guo-Wei Wei:
Artificial intelligence-aided protein engineering: from topological data analysis to deep protein language models. - Yuansheng Liu, Xiangzhen Shen, Yongshun Gong, Yiping Liu, Bosheng Song, Xiangxiang Zeng:
Sequence Alignment/Map format: a comprehensive review of approaches and applications. - Hunyong Cho, Yixiang Qu, Chuwen Liu, Boyang Tang, Ruiqi Lyu, Bridget M. Lin, Jeffrey Roach, M. Andrea Azcarate-Peril, Apoena Aguiar Ribeiro, Michael I. Love, Kimon Divaris, Di Wu:
Comprehensive evaluation of methods for differential expression analysis of metatranscriptomics data. - Wenlan Chen, Hong Wang, Cheng Liang:
Deep multi-view contrastive learning for cancer subtype identification. - Weixu Wang, Xiaolan Zhou, Jing Wang, Jun Yao, Haimei Wen, Yi Wang, Mingwan Sun, Chao Zhang, Wei Tao, Jiahua Zou, Ting Ni:
Approximate estimation of cell-type resolution transcriptome in bulk tissue through matrix completion. - Nan Sheng, Yan Wang, Lan Huang, Ling Gao, Yangkun Cao, Xuping Xie, Yuan Fu:
Multi-task prediction-based graph contrastive learning for inferring the relationship among lncRNAs, miRNAs and diseases. - S. M. Bargeen Alam Turzo, Justin T. Seffernick, Sergey Lyskov, Steffen Lindert:
Predicting ion mobility collision cross sections using projection approximation with ROSIE-PARCS webserver. - Tasbiraha Athaya, Rony Chowdhury Ripan, Xiaoman Li, Haiyan Hu:
Multimodal deep learning approaches for single-cell multi-omics data integration. - Marc Horlacher, Giulia Cantini, Julian Hesse, Patrick Schinke, Nicolas Goedert, Shubhankar Londhe, Lambert Moyon, Annalisa Marsico:
A systematic benchmark of machine learning methods for protein-RNA interaction prediction. - Hongyan Gao, Jianqiang Sun, Yukun Wang, Yuer Lu, Liyu Liu, Qi Zhao, Jianwei Shuai:
Predicting metabolite-disease associations based on auto-encoder and non-negative matrix factorization. - Yang Yue, Shu Li, Lingling Wang, Huanxiang Liu, Henry H. Y. Tong, Shan He:
MpbPPI: a multi-task pre-training-based equivariant approach for the prediction of the effect of amino acid mutations on protein-protein interactions. - Huiguang Yi, Yanling Lin, Qing Chang, Wenfei Jin:
A fast and globally optimal solution for RNA-seq quantification. - Tong Liu, Zheng Wang:
HiC4D: forecasting spatiotemporal Hi-C data with residual ConvLSTM. - Md. Rezaul Karim, Tanhim Islam, Md Shajalal, Oya Beyan, Christoph Lange, Michael Cochez, Dietrich Rebholz-Schuhmann, Stefan Decker:
Explainable AI for Bioinformatics: Methods, Tools and Applications. - Mona Nourbakhsh, Astrid Saksager, Nikola Tom, Xi Steven Chen, Antonio Colaprico, Catharina Olsen, Matteo Tiberti, Elena Papaleo:
A workflow to study mechanistic indicators for driver gene prediction with Moonlight. - Jun Wang, Marc Horlacher, Lixin Cheng, Ole Winther:
RNA trafficking and subcellular localization - a review of mechanisms, experimental and predictive methodologies. - Nguyen-Quoc-Khanh Le, Wanru Li, Yanshuang Cao:
Sequence-based prediction model of protein crystallization propensity using machine learning and two-level feature selection. - Kai Chen, Xiaodong Zhu, Jiahao Wang, Ziqi Zhao, Lei Hao, Xinsheng Guo, Yuanning Liu:
MFPred: prediction of ncRNA families based on multi-feature fusion. - Tianjiao Zhang, Liangyu Li, Hailong Sun, Dali Xu, Guohua Wang:
DeepICSH: a complex deep learning framework for identifying cell-specific silencers and their strength from the human genome. - Shijie Xie, Xiaojun Xie, Xin Zhao, Fei Liu, Yiming Wang, Jihui Ping, Zhiwei Ji:
HNSPPI: a hybrid computational model combing network and sequence information for predicting protein-protein interaction. - Jianfeng Sun, Arulsamy Kulandaisamy, Jinlong Ru, M. Michael Gromiha, Adam P. Cribbs:
TMKit: a Python interface for computational analysis of transmembrane proteins. - Tingting Zhao, Lihua Cao, Jiafu Ji, David K. Chang, Jianmin Wu:
ReProMSig: an integrative platform for development and application of reproducible multivariable models for cancer prognosis supporting guideline-based transparent reporting. - Runzhou Yu, Syed Muhammad Umer Abdullah, Yanni Sun:
HMMPolish: a coding region polishing tool for TGS-sequenced RNA viruses. - Zhongying Ru, Yangyang Wu, Jinning Shao, Jianwei Yin, Linghui Qian, Xiaoye Miao:
A dual-modal graph learning framework for identifying interaction events among chemical and biotech drugs. - Bo Wang, Jiawei Luo, Ying Liu, Wanwan Shi, Zehao Xiong, Cong Shen, Yahui Long:
Spatial-MGCN: a novel multi-view graph convolutional network for identifying spatial domains with attention mechanism. - Jeonghyeon Gu, Dongmin Bang, Jungseob Yi, Sangseon Lee, Dong Kyu Kim, Sun Kim:
A model-agnostic framework to enhance knowledge graph-based drug combination prediction with drug-drug interaction data and supervised contrastive learning. - Andrea Raffo, Jonas Paulsen:
The shape of chromatin: insights from computational recognition of geometric patterns in Hi-C data. - Qingyong Wang, Minfan He, Long-Yi Guo, Hua Chai:
AFEI: adaptive optimized vertical federated learning for heterogeneous multi-omics data integration. - Lindsay Lee, Miao Yu, Xiaoqi Li, Chenxu Zhu, Yanxiao Zhang, Hongyu Yu, Ziyin Chen, Shreya Mishra, Bing Ren, Yun Li, Ming Hu:
SnapHiC-D: a computational pipeline to identify differential chromatin contacts from single-cell Hi-C data. - Jun Wu, Haipeng Qing, Jian Ouyang, Jiajia Zhou, Zihao Gao, Christopher E. Mason, Zhichao Liu, Tieliu Shi:
HiFun: homology independent protein function prediction by a novel protein-language self-attention model. - Yanping Zeng, Yongxin He, Ruiqing Zheng, Min Li:
Inferring single-cell gene regulatory network by non-redundant mutual information. - Jian Gao, Zheyuan Shen, Yufeng Xie, Jialiang Lu, Yang Lu, Sikang Chen, Qingyu Bian, Yue Guo, Liteng Shen, Jian Wu, Binbin Zhou, Tingjun Hou, Qiaojun He, Jinxin Che, Xiaowu Dong:
TransFoxMol: predicting molecular property with focused attention. - Shen Han, Haitao Fu, Yuyang Wu, Ganglan Zhao, Zhenyu Song, Feng Huang, Zhongfei Zhang, Shichao Liu, Wen Zhang:
HimGNN: a novel hierarchical molecular graph representation learning framework for property prediction. - Hai Yang, Yawen Liu, Yijing Yang, Dongdong Li, Zhe Wang:
InDEP: an interpretable machine learning approach to predict cancer driver genes from multi-omics data. - Na Lu, Yi Qiao, Pengfei An, Jiajian Luo, Changwei Bi, Musheng Li, Zuhong Lu, Jing Tu:
Exploration of whole genome amplification generated chimeric sequences in long-read sequencing data. - Robert Wang, Ingo Helbig, Andrew C. Edmondson, Lan Lin, Yi Xing:
Splicing defects in rare diseases: transcriptomics and machine learning strategies towards genetic diagnosis. - Yan Li, Shao-Wu Zhang, Ming-Yu Xie, Tong Zhang:
PhenoDriver: interpretable framework for studying personalized phenotype-associated driver genes in breast cancer. - Pingjing Li, Hong Liu, Jialiang Sun, Jianguo Lu, Jian Liu:
HiBrowser: an interactive and dynamic browser for synchronous Hi-C data visualization. - Yao-zhong Zhang, Yunjie Liu, Zeheng Bai, Kosuke Fujimoto, Satoshi Uematsu, Seiya Imoto:
Zero-shot-capable identification of phage-host relationships with whole-genome sequence representation by contrastive learning. - Xuejing Shi, Juntong Zhu, Yahui Long, Cheng Liang:
Identifying spatial domains of spatially resolved transcriptomics via multi-view graph convolutional networks. - Quentin Blampey, Nadège Bercovici, Charles-Antoine Dutertre, Isabelle Pic, Joana Mourato Ribeiro, Fabrice André, Paul-Henry Cournède:
A biology-driven deep generative model for cell-type annotation in cytometry. - Shudong Wang, Yunyin Li, Yuanyuan Zhang, Shanchen Pang, Sibo Qiao, Yu Zhang, Fuyu Wang:
Generative Adversarial Matrix Completion Network based on Multi-Source Data Fusion for miRNA-Disease Associations Prediction. - Lin Li, Rui Xia, Wei Chen, Qi Zhao, Peng Tao, Luonan Chen:
Single-cell causal network inferred by cross-mapping entropy.
Volume 24, Number 6, September 2023
- Wentai Ma, Leisheng Shi, Mingkun Li:
A fast and accurate method for SARS-CoV-2 genomic tracing. - Yongjian Yang, Yu-Te Lin, Guanxun Li, Yan Zhong, Qian Xu, James J. Cai:
Interpretable modeling of time-resolved single-cell gene-protein expression with CrossmodalNet. - Takako Mochizuki, Mika Sakamoto, Yasuhiro Tanizawa, Takuro Nakayama, Goro Tanifuji, Ryoma Kamikawa, Yasukazu Nakamura:
A practical assembly guideline for genomes with various levels of heterozygosity. - Pengfei Gao, Haonan Zhao, Zheng Luo, Yifan Lin, Wanjie Feng, Yaling Li, Fanjiang Kong, Xia Li, Chao Fang, Xutong Wang:
SoyDNGP: a web-accessible deep learning framework for genomic prediction in soybean breeding. - Zhuo Wang, Yuxuan Pang, Chia-Ru Chung, Hsin-Yao Wang, Haiyan Cui, Ying-Chih Chiang, Jorng-Tzong Horng, Jang-Jih Lu, Tzong-Yi Lee:
A risk assessment framework for multidrug-resistant Staphylococcus aureus using machine learning and mass spectrometry technology. - Wentao Zhang, Yanchao Xu, Aowen Wang, Gang Chen, Junbo Zhao:
Fuse feeds as one: cross-modal framework for general identification of AMPs. - Zhe Lin, Yangmei Qin, Hao Chen, Dan Shi, Mindong Zhong, Te An, Linshan Chen, Yiquan Wang, Fan Lin, Guang Li, Zhi-Liang Ji:
TransIntegrator: capture nearly full protein-coding transcript variants via integrating Illumina and PacBio transcriptomes. - Andy J. Wu, Akila Perera, Linganesan Kularatnarajah, Anna Korsakova, Jason J. Pitt:
Mutational signature assignment heterogeneity is widespread and can be addressed by ensemble approaches. - Saisai Tian, Yanan Li, Jia Xu, Lijun Zhang, Jinbo Zhang, Jinyuan Lu, Xike Xu, Xin Luan, Jing Zhao, Weidong Zhang:
COIMMR: a computational framework to reveal the contribution of herbal ingredients against human cancer via immune microenvironment and metabolic reprogramming. - Musun Park, Su-Jin Baek, Sang-Min Park, Jin-Mu Yi, Seongwon Cha:
Comparative study of the mechanism of natural compounds with similar structures using docking and transcriptome data for improving in silico herbal medicine experimentations. - Yu-Ning Huang, Mohammad Vahed, Kerui Peng, Houda Alachkar, Serghei Mangul:
Response to 'comment on rigorous benchmarking of T cell receptor repertoire profiling methods for cancer RNA sequencing' by Davydov A.N.; Bolotin D.A.; Poslavsky S. V. and Chudakov D.M. - Alison K. Adams, Brandon D. Kristy, Myranda Gorman, Peter Balint-Kurti, G. Craig Yencho, Bode A. Olukolu:
Qmatey: an automated pipeline for fast exact matching-based alignment and strain-level taxonomic binning and profiling of metagenomes. - Yidong Song, Qianmu Yuan, Huiying Zhao, Yuedong Yang:
Accurately identifying nucleic-acid-binding sites through geometric graph learning on language model predicted structures. - Marie-Pier Scott-Boyer, Pascal Dufour, François Belleau, Régis Ongaro-Carcy, Clément Plessis, Olivier Périn, Arnaud Droit:
Use of Elasticsearch-based business intelligence tools for integration and visualization of biological data. - Anupam Gautam, Debaleena Bhowmik, Sayantani Basu, Wenhuan Zeng, Abhishake Lahiri, Daniel H. Huson, Sandip Paul:
Microbiome Metabolome Integration Platform (MMIP): a web-based platform for microbiome and metabolome data integration and feature identification. - Haochen Li, Tianxing Ma, Minsheng Hao, Wenbo Guo, Jin Gu, Xuegong Zhang, Lei Wei:
Decoding functional cell-cell communication events by multi-view graph learning on spatial transcriptomics. - Alexey N. Davydov, Dmitry Bolotin, Stanislav V. Poslavsky, Dmitry M. Chudakov:
Comment on 'rigorous benchmarking of T cell receptor repertoire profiling methods for cancer RNA sequencing'. - Antonino Fiannaca, Massimo La Rosa, Laura La Paglia, Salvatore Gaglio, Alfonso Urso:
GOWDL: gene ontology-driven wide and deep learning model for cell typing of scRNA-seq data. - Ziyang Tang, Xiang Liu, Zuotian Li, Tonglin Zhang, Baijian Yang, Jing Su, Qianqian Song:
SpaRx: elucidate single-cell spatial heterogeneity of drug responses for personalized treatment. - Correction to: An automatic immunofluorescence pattern classification framework for HEp-2 image based on supervised learning.
- Chao Xu, Runduo Liu, Shuheng Huang, Wenchao Li, Zhe Li, Hai-Bin Luo:
3D-SMGE: a pipeline for scaffold-based molecular generation and evaluation. - Jie Pan, Zhuhong You, Wencai You, Tian Zhao, Chenlu Feng, Xuexia Zhang, Fengzhi Ren, Sanxing Ma, Fan Wu, Shiwei Wang, Yanmei Sun:
PTBGRP: predicting phage-bacteria interactions with graph representation learning on microbial heterogeneous information network. - Kai Jing, Yewen Xu, Yang Yang, Pengfei Yin, Duo Ning, Guangyu Huang, Yuqing Deng, Gengzhan Chen, Guoliang Li, Simon Zhongyuan Tian, Meizhen Zheng:
ScSmOP: a universal computational pipeline for single-cell single-molecule multiomics data analysis. - Ying Wang, Min Zhang, Jian Shi, Yue Zhu, Xin Wang, Shaojun Zhang, Fang Wang:
Cracking the pattern of tumor evolution based on single-cell copy number alterations. - Tianyuan Lei, Ruoyu Chen, Shaoqiang Zhang, Yong Chen:
Self-supervised deep clustering of single-cell RNA-seq data to hierarchically detect rare cell populations. - Ran Zhang, Xuezhi Wang, Pengfei Wang, Zhen Meng, Wenjuan Cui, Yuanchun Zhou:
HTCL-DDI: a hierarchical triple-view contrastive learning framework for drug-drug interaction prediction. - Haoran Sun, Zhaoqi Song, Qiuming Chen, Meiling Wang, Furong Tang, Lijun Dou, Quan Zou, Fenglong Yang:
MMiKG: a knowledge graph-based platform for path mining of microbiota-mental diseases interactions. - Haonan Wu, Jiyun Han, Shizhuo Zhang, Xin Gao, Chaozhou Mou, Juntao Liu:
Spatom: a graph neural network for structure-based protein-protein interaction site prediction. - Mingguang Shi, Xuefeng Li, Mingna Li, Yichong Si:
Attention-based generative adversarial networks improve prognostic outcome prediction of cancer from multimodal data. - Guishan Zhang, Ye Luo, Xianhua Dai, Zhiming Dai:
Benchmarking deep learning methods for predicting CRISPR/Cas9 sgRNA on- and off-target activities.
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