Bioinformatics Research and Applications (eBook, PDF)

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

• Format: PDF

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.

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Produktdetails

• Verlag: Springer Nature Singapore
• Seitenzahl: 147
• Erscheinungstermin: 11. Juli 2024
• Englisch
• ISBN-13: 9789819750870
• Artikelnr.: 72242720

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.