Hypergraph models of biological networks to identify genes critical to pathogenic viral response

Abstract Background Representing biological networks as graphs is a powerful approach to reveal underlying patterns, signatures, and critical components from high-throughput biomolecular data. However, graphs do not natively capture the multi-way relationships present among genes and proteins in bio...

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Библиографические подробности

Главные авторы:	Song Feng, Emily Heath, Brett Jefferson, Cliff Joslyn, Henry Kvinge, Hugh D. Mitchell, Brenda Praggastis, Amie J. Eisfeld, Amy C. Sims, Larissa B. Thackray, Shufang Fan, Kevin B. Walters, Peter J. Halfmann, Danielle Westhoff-Smith, Qing Tan, Vineet D. Menachery, Timothy P. Sheahan, Adam S. Cockrell, Jacob F. Kocher, Kelly G. Stratton, Natalie C. Heller, Lisa M. Bramer, Michael S. Diamond, Ralph S. Baric, Katrina M. Waters, Yoshihiro Kawaoka, Jason E. McDermott, Emilie Purvine
Формат:	Artigo
Язык:	Inglês
Опубликовано:	BMC 2021-05-01
Серии:	BMC Bioinformatics
Предметы:	Systems biology Hypergraph Viral infection Biological networks SARS MERS
Online-ссылка:	https://doi.org/10.1186/s12859-021-04197-2
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