A hybrid deep forest-based method for predicting synergistic drug combinations.

Wu, Lianlian; Gao, Jie; Zhang, Yixin; Sui, Binsheng; Wen, Yuqi; Wu, Qingqiang; Liu, Kunhong; He, Song; Bo, Xiaochen

Wu, Lianlian; Gao, Jie; Zhang, Yixin; Sui, Binsheng; Wen, Yuqi; Wu, Qingqiang; Liu, Kunhong; He, Song; Bo, Xiaochen.

Afiliación

Wu L; Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China.
Gao J; Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China.
Zhang Y; Department of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou 350122, China.
Sui B; Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China.
Wen Y; School of Film, Xiamen University, Xiamen 361005, China.
Wu Q; Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China.
Liu K; School of Film, Xiamen University, Xiamen 361005, China.
He S; School of Film, Xiamen University, Xiamen 361005, China.
Bo X; Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China.

Cell Rep Methods ; 3(2): 100411, 2023 02 27.

Article en En | MEDLINE | ID: mdl-36936075

Asunto(s)

Biología Computacional; Neoplasias; Humanos; Biología Computacional/métodos; Neoplasias/tratamiento farmacológico; Combinación de Medicamentos; Línea Celular

Palabras clave

class imbalance; deep forest; deep learning; drug combination prediction; synergy

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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Biología Computacional / Neoplasias Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Cell Rep Methods Año: 2023 Tipo del documento: Article País de afiliación: China

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