SSELM-neg: spherical search-based extreme learning machine for drug-target interaction prediction.

Hu, Lingzhi; Fu, Chengzhou; Ren, Zhonglu; Cai, Yongming; Yang, Jin; Xu, Siwen; Xu, Wenhua; Tang, Deyu

Hu, Lingzhi; Fu, Chengzhou; Ren, Zhonglu; Cai, Yongming; Yang, Jin; Xu, Siwen; Xu, Wenhua; Tang, Deyu.

Afiliación

Hu L; School of Medical Information Engineering, Guangdong Pharmaceutical University, Guangzhou, People's Republic of China.
Fu C; School of Medical Information Engineering, Guangdong Pharmaceutical University, Guangzhou, People's Republic of China.
Ren Z; Guangdong Province Precise Medicine Big Data of Traditional Chinese Medicine Engineering Technology Research Center, Guangzhou, People's Republic of China.
Cai Y; School of Medical Information Engineering, Guangdong Pharmaceutical University, Guangzhou, People's Republic of China.
Yang J; School of Medical Information Engineering, Guangdong Pharmaceutical University, Guangzhou, People's Republic of China.
Xu S; Guangdong Province Precise Medicine Big Data of Traditional Chinese Medicine Engineering Technology Research Center, Guangzhou, People's Republic of China.
Xu W; School of Medical Information Engineering, Guangdong Pharmaceutical University, Guangzhou, People's Republic of China.
Tang D; Guangdong Province Precise Medicine Big Data of Traditional Chinese Medicine Engineering Technology Research Center, Guangzhou, People's Republic of China.

BMC Bioinformatics ; 24(1): 38, 2023 Feb 03.

Article en En | MEDLINE | ID: mdl-36737694

Asunto(s)

Desarrollo de Medicamentos; Descubrimiento de Drogas; Descubrimiento de Drogas/métodos; Aprendizaje Automático; Algoritmos; Interacciones Farmacológicas

Palabras clave

Class imbalance; Drug discovery; Drugtarget interactions; Extreme learning machine; Spherical search

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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Descubrimiento de Drogas / Desarrollo de Medicamentos Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: BMC Bioinformatics Asunto de la revista: INFORMATICA MEDICA Año: 2023 Tipo del documento: Article

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