Overview

This book constitutes the refereed proceedings of the 20th International Symposium on Bioinformatics Research and Applications, ISBRA 2024, held in Kunming, China, in July 19–21, 2024. The 93 full papers  included in this book were carefully reviewed and selected from 236 submissions. The symposium provides a forum for the exchange of ideas and results among researchers, developers, and practitioners working on all aspects of bioinformatics and computational biology and their applications.

Full Product Details

Author:   Wei Peng ,  Zhipeng Cai ,  Pavel Skums
Publisher:   Springer Verlag, Singapore
Imprint:   Springer Nature
Edition:   2024 ed.
Volume:   14956
ISBN:  

9789819750863

ISBN 10:   9819750865
Pages:   147
Publication Date:   10 July 2024
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand  
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Table of Contents

.- Feddaw: Dual Adaptive Weighted Federated Learning for Non-IID Medical Data. .- LoopNetica: predicting chromatin loops using convolutional neural networks and attention mechanisms. .- Probabilistic and Machine Learning Models for the Protein Scaffold Gap Filling Problem. .- Patient Anticancer Drug Response Prediction based on Single-Cell Deconvolution. .- A Data Set of Paired Structural Segments between Protein Data Bank and AlphaFold DB for Medium-Resolution Cryo-EM Density Maps: A Gap in Overall Structural Quality. .- PmmNDD: Predicting the Pathogenicity of Missense Mutations in Neurodegenerative Diseases via Ensemble Learning. .- Improved Inapproximability Gap and Approximation Algorithm for Scaffold Filling to Maximize Increased Duo-preservations. .- Residual Spatio-Temporal Attention based Prototypical Network for Rare Arrhythmia Classification. .- SEMQuant: Extending Sipros-Ensemble with Match-Between-Runs for comprehensive quantitative metaproteomics. .- PrSMBooster：Improving the Accuracy of Top-down Proteoform Characterization using Deep Learning Rescoring Models. .- FCMEDriver: identifing cancer driver gene by combining mutual exclusivity of embedded features and optimized mutation frequency score.

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