Peng / Cai / Skums Bioinformatics Research and Applications
Erscheinungsjahr 2024
ISBN: 978-981-97-5087-0
Verlag: Springer Singapore
Format: PDF
Kopierschutz: 1 - PDF Watermark
20th International Symposium, ISBRA 2024, Kunming, China, July 19–21, 2024, Proceedings, Part III
E-Book, Englisch, 147 Seiten
Reihe: Lecture Notes in Bioinformatics
ISBN: 978-981-97-5087-0
Verlag: Springer Singapore
Format: PDF
Kopierschutz: 1 - PDF Watermark
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.
Zielgruppe
Research
Autoren/Hrsg.
Weitere Infos & Material
.- 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.
