Computational Advances in Bio and Medical Sciences

​Computational Advances in Bio and Medical Sciences.- Single Model Quality Estimation of Protein Structures via Non-negative Tensor Factorization.- Graph Representation Learning for Protein Conformation Sampling.- Excerno: Filtering mutations caused by the clinical archival process in sequencing data.- Relabeling metabolic pathway data with groups to improve prediction outcomes.- MELEPS: Multiple Expert Linear Epitope Prediction System.- Encoder-Decoder Architectures for Clinically Relevant Coronary Artery Segmentation.- Unified SAT-Solving for Hard Problems of Phylogenetic Network Construction.- Feature Selection for Identification of Risk Factors Associated with Infant Mortality.- Addressing classification on highly imbalanced clinical datasets.- mcPBWT: Space-efficient Multi-column PBWT Scanning Algorithm for Composite Haplotype Matching.- Computational Advances in Molecular Epidemiology.- Clustering SARS-CoV-2 Variants from Raw High-Throughput Sequencing Reads Data.- Analysis of SARS-CoV-2 temporal molecular networks using global and local topological characteristics.- An SVM Based Approach to Study the Racial Disparity in Triple-Negative Breast Cancer.