Research in Computational Molecular Biology

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Details

This book constitutes the refereed proceedings of the 27th Annual International Conference on Research in Computational Molecular Biology, RECOMB 2023, held in Istanbul, Turkey, during April 1619, 2023.
The 11 regular and 33 short papers presented in this book were carefully reviewed and selected from 188 submissions. The papers report on original research in all areas of computational molecular biology and bioinformatics.

Inhalt
VStrains: De Novo Reconstruction of Viral Strains via Iterative Path Extraction From Assembly Graphs.- Spectrum preserving tilings enable sparse and modular reference indexing.- Statistically Consistent Rooting of Species Trees under the Multispecies Coalescent Model.- Sequence to graph alignment using gap-sensitive co-linear chaining.- DM-Net: A Dual-Model Network for Automated Biomedical Image Diagnosis.- MTGL-ADMET: A Novel Multi-Task Graph Learning Framework for ADMET Prediction Enhanced by Status-Theory and Maximum Flow.- CDGCN: Conditional de novo Drug generative model using Graph Convolution Networks.- Percolate: an exponential family JIVE model to design DNA-based predictors of drug response.- Translation rate prediction and regulatory motif discovery with multi-task learning.- Computing shortest hyperpaths for pathway inference in cellular reaction networks.- T-Cell Receptor Optimization with Reinforcement Learning and MutationPolices for Precision Immunotherapy.

Weitere Informationen

  • Allgemeine Informationen
    • GTIN 09783031291180
    • Herausgeber Springer Nature Switzerland
    • Anzahl Seiten 304
    • Lesemotiv Verstehen
    • Genre Software
    • Auflage 1st edition 2023
    • Editor Haixu Tang
    • Sprache Englisch
    • Gewicht 464g
    • Untertitel 27th Annual International Conference, RECOMB 2023, Istanbul, Turkey, April 16-19, 2023, Proceedings
    • Größe H235mm x B155mm x T17mm
    • Jahr 2023
    • EAN 9783031291180
    • Format Kartonierter Einband
    • ISBN 3031291182
    • Veröffentlichung 24.03.2023
    • Titel Research in Computational Molecular Biology

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