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ORCID12th ICCABS 2023: Norman, OK, USA
- Mukul S. Bansal, Wei Chen, Yury Khudyakov, Ion I. Mandoiu, Marmar Moussa
, Murray Patterson, Sanguthevar Rajasekaran, Pavel Skums, Sharma V. Thankachan, Alexander Zelikovsky:
Computational Advances in Bio and Medical Sciences - 12th International Conference, ICCABS 2023, Norman, OK, USA, December 11-13, 2023, Revised Selected Papers. Lecture Notes in Computer Science 14548, Springer 2025, ISBN 978-3-031-82767-9 - Konstantinos Barzas, Shereen Fouad, Gainer Jasa, Gabriel Landini:
An Explainable Deep Learning Framework for Mandibular Canal Segmentation from Cone Beam Computed Tomography Volumes. 1-13 - Paola Bonizzoni, Clelia De Felice, Yuri Pirola, Raffaella Rizzi, Rocco Zaccagnino, Rosalba Zizza:
Identification of Chimeric RNAs: A Novel Machine Learning Perspective. 14-26 - Aysegul Bumin, Kejun Huang, Tamer Kahveci:
PartialFibers: An Efficient Method for Predicting Drug-Drug Interactions. 27-39 - Ayush Chaturvedi, Guohua Cao, Wu-chun Feng:
Optimizing Deep Learning for Biomedical Imaging. 40-52 - Chu-Yu Cheng, Chung-Chin Lu:
Exploring a Solution Curve in the Phase Plane for Extreme Firing Rates in the Izhikevich Model. 53-64 - Julia Cordes, Jaime I. Dávila:
Cancer and Tissue Prediction Using Mutational Signatures in Highly Mutated Cancers. 65-74 - Arnab Ganguly, Daniel Gibney, Arghya Kusum Das, Sharma V. Thankachan:
On the Hardness of Wildcard Pattern Matching on de Bruijn Graphs. 75-81 - Akshay Juyal, Zahra Tayebi, Alexander Zelikovsky, Mauricio Soto Gomez, Simone Ciccolella, Gianluca Della Vedova, Murray Patterson:
Plastic: An Easy to Use and Modular Tool for Benchmarking Tumor Phylogeny Reconstruction Pipelines. 82-93 - Armen Kasparian, Guohua Cao, Wu-chun Feng:
A 3D Deep Learning Architecture for Denoising Low-Dose CT Scans. 94-106 - Lucas Otávio L. Silva, Karine Marques Hara, Pedro Henrique Mendes de Paula, Alexandre Rossi Paschoal, Fabrício Martins Lopes:
A Simple and Interpretable Deep Learning Model for Diagnosing Pneumonia from Chest X-Ray Images. 107-118 - Bin Li, Hongchang Gao, Xinghua Shi:
FedDP: Secure Federated Learning with Differential Privacy for Disease Prediction. 119-131 - Marmar Moussa, Charles H. Street, Sriram Boddeda:
Computational Tumor Progression Analysis via Seriation Based Trajectory Inference. 132-144 - Sean M. Nguyen, Mohammad Amin Basiri, Sina Khanmohammadi:
Multilayer Network Analysis of Brain Signals for Detecting Alzheimer's Disease. 145-153 - Sri Lakshmi Bhavani Pagolu, S. Suba, Nita Parekh:
DNA Methylation Based Subtype Classification of Breast Cancer. 154-165 - Namitha Pais, Nalini Ravishanker, Sanguthevar Rajasekaran, George Weinstock:
Repeated Measures Latent Dirichlet Allocation for Longitudinal Microbiome Analysis. 166-177 - Xihan Qin, Li Liao:
Improving Disease Comorbidity Prediction with Biologically Supervised Graph Embedding. 178-190 - Suba Suseela, Nita Parekh:
Lightweight and Generalizable Model for COVID-19 Detection Using Chest Xray Images. 191-202 - Bikram Sahoo, Nikita Jinna, Padmashree Rida, Zandra Pinnix, Alex Zelikovsky:
Decoding Heterogeneity in Quadruple-Negative Breast Cancer: A Data-driven Clustering Approach. 203-217 - Rahul Singh, Jiadong Yu:
Determining Temporal Linkages in Dynamic Epidemiological Networks Using the Earth Mover's Distance. 218-228 - Emma T. Stubby, Seyed Majid Razavi, Sina Khanmohammadi:
Functional Connectivity Disruptions in Alzheimer's Disease: A Maximum Flow Perspective. 229-237 - Francesco Tomasella, Cinzia Pizzi:
On Multi-phase Metagenomics Reads Binning. 238-250 - Ankur Yadav, Ovidiu Daescu:
A Unified Machine Learning Framework for Multi-subtype Tumour Classification Across Diverse Datasets. 251-261 - Chenyu Zhang, Honglin Wang, Seung-Hyun Hong, Riqiang Yan, Dong-Guk Shin:
AFA: Abstract Functional Analysis Identifies New Microglial Subtypes at Single Cell Level in Alzheimer's Disease. 262-276
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