A guided network propagation approach to identify disease genes that combines prior and new information

Borislav H. Hristov, Bernard Chazelle, Mona Singh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Summary. A major challenge in biomedical data science is to identify the causal genes underlying complex genetic diseases. Despite the massive influx of genome sequencing data, identifying disease-relevant genes remains difficult as individuals with the same disease may share very few, if any, genetic variants.

Original languageEnglish (US)
Title of host publicationResearch in Computational Molecular Biology - 24th Annual International Conference, RECOMB 2020, Proceedings
EditorsRussell Schwartz
PublisherSpringer
Pages251-252
Number of pages2
ISBN (Print)9783030452568
DOIs
StatePublished - Jan 1 2020
Event24th Annual Conference on Research in Computational Molecular Biology, RECOMB 2020 - Padua, Italy
Duration: May 10 2020May 13 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12074 LNBI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference24th Annual Conference on Research in Computational Molecular Biology, RECOMB 2020
CountryItaly
CityPadua
Period5/10/205/13/20

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Hristov, B. H., Chazelle, B., & Singh, M. (2020). A guided network propagation approach to identify disease genes that combines prior and new information. In R. Schwartz (Ed.), Research in Computational Molecular Biology - 24th Annual International Conference, RECOMB 2020, Proceedings (pp. 251-252). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12074 LNBI). Springer. https://doi.org/10.1007/978-3-030-45257-5_25