Bioinformatics Research and Applications

20th International Symposium, ISBRA 2024, Kunming, China, July 19-21, 2024, Proceedings, Part III
Herausgegeben:Peng, Wei; Cai, Zhipeng; Skums, Pavel

• Broschiertes Buch

Produktbeschreibung

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.

Produktdetails

• Lecture Notes in Computer Science 14956
• Verlag: Springer / Springer Nature Singapore / Springer, Berlin
• Artikelnr. des Verlages: 978-981-97-5086-3
• 2024
• Seitenzahl: 164
• Erscheinungstermin: 10. Juli 2024
• Englisch
• Abmessung: 235mm x 155mm x 10mm
• Gewicht: 260g
• ISBN-13: 9789819750863
• ISBN-10: 9819750865
• Artikelnr.: 70921550

Inhaltsangabe

.- 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.