A Data-Driven Signaling Network Inference Approach for Phosphoproteomics.

Madison, Imani; Amin, Fin; Song, Kuncheng; Sozzani, Rosangela; Van den Broeck, Lisa

Madison, Imani; Amin, Fin; Song, Kuncheng; Sozzani, Rosangela; Van den Broeck, Lisa.

Afiliación

Madison I; Department of Plant and Microbial Biology and NC Plant Sciences Initiative, North Carolina State University, Raleigh, NC, USA.
Amin F; Department of Electrical and Computer Engineering, North Carolina State University, Raleigh, NC, USA.
Song K; Bioinformatics Research Center, North Carolina State University, Raleigh, NC, USA.
Sozzani R; Department of Plant and Microbial Biology and NC Plant Sciences Initiative, North Carolina State University, Raleigh, NC, USA. ross_sozzani@ncsu.edu.
Van den Broeck L; Department of Plant and Microbial Biology and NC Plant Sciences Initiative, North Carolina State University, Raleigh, NC, USA. lfvanden@ncsu.edu.

Methods Mol Biol ; 2690: 335-354, 2023.

Article en En | MEDLINE | ID: mdl-37450158

Asunto(s)

Fosfoproteínas; Proteómica; Teorema de Bayes; Fosfoproteínas/metabolismo; Proteómica/métodos; Transducción de Señal; Fosforilación; Fosfotransferasas/metabolismo; Monoéster Fosfórico Hidrolasas/metabolismo

Palabras clave

Bayesian modeling; Kinase regulatory networks; Label-free phosphoproteomics; Post-translational modifications

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Texto completo: 1 Base de datos: MEDLINE Asunto principal: Fosfoproteínas / Proteómica Tipo de estudio: Prognostic_studies Idioma: En Revista: Methods Mol Biol Asunto de la revista: BIOLOGIA MOLECULAR Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos

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