Computational Advances in Bio and Medical Sciences (eBook, PDF)

11th International Conference, ICCABS 2021, Virtual Event, December 16-18, 2021, Revised Selected Papers
Redaktion: Bansal, Mukul S.; Zelikovsky, Alexander; Skums, Pavel; Rajasekaran, Sanguthevar; Patterson, Murray; Moussa, Marmar; Mandoiu, Ion

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: Springer International Publishing
• Seitenzahl: 177
• Erscheinungstermin: 18. Oktober 2022
• Englisch
• ISBN-13: 9783031175312
• Artikelnr.: 66208506

Inhaltsangabe

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.

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