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

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

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 biological...

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Main Authors: Baric, R.S (Author), Bramer, L.M (Author), Cockrell, A.S (Author), Diamond, M.S (Author), Eisfeld, A.J (Author), Fan, S. (Author), Feng, S. (Author), Halfmann, P.J (Author), Heath, E. (Author), Heller, N.C (Author), Jefferson, B. (Author), Joslyn, C. (Author), Kawaoka, Y. (Author), Kocher, J.F (Author), Kvinge, H. (Author), McDermott, J.E (Author), Menachery, V.D (Author), Mitchell, H.D (Author), Praggastis, B. (Author), Purvine, E. (Author), Sheahan, T.P (Author), Sims, A.C (Author), Stratton, K.G (Author), Tan, Q. (Author), Thackray, L.B (Author), Walters, K.B (Author), Waters, K.M (Author), Westhoff-Smith, D. (Author)
Format: Article
Language:English
Published: BioMed Central Ltd 2021
Subjects:
algorithm
Algorithms
Betweenness centrality
biological model
Biological networks
Biological systems
Complex biological systems
Complex networks
Complex relationships
Critical component
Experimental conditions
Genes
genomics
Genomics
Graph centralities
Graph theory
Host response
Hypergraph
Influenza
MERS
Metabolic reactions
Models, Biological
protein
Proteins
SARS
Systems biology
Viral infection
Viral pathogenesis
Viruses
West Nile Virus
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