Bansal / Mandoiu / Moussa Computational Advances in Bio and Medical Sciences

11th International Conference, ICCABS 2021, Virtual Event, December 16–18, 2021, Revised Selected Papers
Erscheinungsjahr 2022
ISBN: 978-3-031-17531-2
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 177 Seiten

Reihe: Computer Science

This book constitutes revised selected papers from the refereed proceedings of the 11th International Conference on Computational Advances in Bio and Medical Sciences, ICCABS 2021, held as a virtual event during December 16–18, 2021.
The 13 full papers included in this book were carefully reviewed and selected from 17 submissions. They were organized in topical sections as follows: Computational advances in bio and medical sciences; and computational advances in molecular epidemiology.

Zielgruppe

Research

Autoren/Hrsg.

Bansal, Mukul S.

Mandoiu, Ion

Moussa, Marmar

Patterson, Murray

Rajasekaran, Sanguthevar

Skums, Pavel

Zelikovsky, Alexander

Inhaltsverzeichnis

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.