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

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

E-Book, Englisch, 177 Seiten

Reihe: Lecture Notes in 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.