Protein prediction models support widespread post-transcriptional regulation of protein abundance by interacting partners.

Srivastava, Himangi; Lippincott, Michael J; Currie, Jordan; Canfield, Robert; Lam, Maggie P Y; Lau, Edward

Srivastava, Himangi; Lippincott, Michael J; Currie, Jordan; Canfield, Robert; Lam, Maggie P Y; Lau, Edward.

Afiliación

Srivastava H; Department of Medicine/Cardiology, University of Colorado School of Medicine, Aurora, Colorado, United States of America.
Lippincott MJ; Consortium for Fibrosis Research and Translation, University of Colorado School of Medicine, Aurora, Colorado, United States of America.
Currie J; Department of Medicine/Cardiology, University of Colorado School of Medicine, Aurora, Colorado, United States of America.
Canfield R; Department of Medicine/Cardiology, University of Colorado School of Medicine, Aurora, Colorado, United States of America.
Lam MPY; Department of Medicine/Cardiology, University of Colorado School of Medicine, Aurora, Colorado, United States of America.
Lau E; Department of Medicine/Cardiology, University of Colorado School of Medicine, Aurora, Colorado, United States of America.

PLoS Comput Biol ; 18(11): e1010702, 2022 11.

Article en En | MEDLINE | ID: mdl-36356032

Asunto(s)

Proteoma; Proteómica; Proteoma/metabolismo; Proteómica/métodos; Regulación de la Expresión Génica; Transcriptoma; ARN Mensajero/genética; ARN Mensajero/metabolismo

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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Proteoma / Proteómica Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: PLoS Comput Biol Asunto de la revista: BIOLOGIA / INFORMATICA MEDICA Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos

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