Baldur: Bayesian Hierarchical Modeling for Label-Free Proteomics with Gamma Regressing Mean-Variance Trends.

Berg, Philip; Popescu, George

Berg, Philip; Popescu, George.

Afiliação

Berg P; Institute for Genomics, Biocomputing & Biotechnology, Mississippi State University, Mississippi State, Mississippi, USA; Department of Biochemistry, Molecular Biology, Entomology and Plant Pathology, Mississippi State University, Mississippi State, Mississippi, USA. Electronic address: pb1015@msstate.edu.
Popescu G; Institute for Genomics, Biocomputing & Biotechnology, Mississippi State University, Mississippi State, Mississippi, USA; Department of Biochemistry, Molecular Biology, Entomology and Plant Pathology, Mississippi State University, Mississippi State, Mississippi, USA. Electronic address: popescu@igbb.msstate.edu.

Mol Cell Proteomics ; 22(12): 100658, 2023 Dec.

Article em En | MEDLINE | ID: mdl-37806340

Assuntos

Proteínas; Proteômica; Proteômica/métodos; Teorema de Bayes; Proteínas/química; Peptídeos/metabolismo; Espectrometria de Massas/métodos

Palavras-chave

Bayesian hierarchical model; data-independent acquisition; empirical Bayes; label-free quantification; latent gamma mixture regression; mean-variance trends; proteomics; uncertainty modeling

Texto completo

Imprimir

XML

PubMed Links

Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Proteínas / Proteômica Tipo de estudo: Prognostic_studies Idioma: En Revista: Mol Cell Proteomics Assunto da revista: BIOLOGIA MOLECULAR / BIOQUIMICA Ano de publicação: 2023 Tipo de documento: Article

Texto completo

Imprimir

XML

PubMed Links

Buscar no Google