From multi-omics data to the cancer druggable gene discovery: a novel machine learning-based approach.

Yang, Hai; Gan, Lipeng; Chen, Rui; Li, Dongdong; Zhang, Jing; Wang, Zhe

Yang, Hai; Gan, Lipeng; Chen, Rui; Li, Dongdong; Zhang, Jing; Wang, Zhe.

Afiliación

Yang H; Department of Computer Science and Engineering, East China University of Science and Technology, 200237 Shanghai, PR China.
Gan L; Department of Computer Science and Engineering, East China University of Science and Technology, 200237 Shanghai, PR China.
Chen R; Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, USA.
Li D; Department of Computer Science and Engineering, East China University of Science and Technology, 200237 Shanghai, PR China.
Zhang J; Department of Computer Science and Engineering, East China University of Science and Technology, 200237 Shanghai, PR China.
Wang Z; Department of Computer Science and Engineering, East China University of Science and Technology, 200237 Shanghai, PR China.

Brief Bioinform ; 24(1)2023 01 19.

Article en En | MEDLINE | ID: mdl-36515158

Asunto(s)

Genómica; Neoplasias; Humanos; Genómica/métodos; Multiómica; Variaciones en el Número de Copia de ADN; Neoplasias/tratamiento farmacológico; Neoplasias/genética; Aprendizaje Automático; Estudios de Asociación Genética

Palabras clave

cancer genomics; druggable genome; machine learning; multi-omics

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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Genómica / Neoplasias Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: Brief Bioinform Asunto de la revista: BIOLOGIA / INFORMATICA MEDICA Año: 2023 Tipo del documento: Article

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