Bayesian Inference of Signaling Network Topology in a Cancer Cell Line | Zendy

Steven M. Hill | Zendy; Yiling Lu | Zendy; Jennifer R. Molina | Zendy; Laura M. Heiser | Zendy; Paul T. Spellman | Zendy; Terence P. Speed | Zendy; Joe W. Gray | Zendy; Gordon B. Mills | Zendy; Sach Mukherjee | Zendy

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Bayesian Inference of Signaling Network Topology in a Cancer Cell Line

Author(s) -

Steven M. Hill,

Yiling Lu,

Jennifer R. Molina,

Laura M. Heiser,

Paul T. Spellman,

Terence P. Speed,

Joe W. Gray,

Gordon B. Mills,

Sach Mukherjee

Publication year - 2012

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/bts514

Subject(s) - computer science , inference , bayesian network , context (archaeology) , bayes' theorem , network topology , dynamic bayesian network , biological network , graphical model , machine learning , approximate bayesian computation , artificial intelligence , computational biology , bayesian probability , biology , computer network , paleontology

Protein signaling networks play a key role in cellular function, and their dysregulation is central to many diseases, including cancer. To shed light on signaling network topology in specific contexts, such as cancer, requires interrogation of multiple proteins through time and statistical approaches to make inferences regarding network structure.

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