Predicting potential microbe-disease associations based on multi-source features and deep learning.

Wang, Liugen; Wang, Yan; Xuan, Chenxu; Zhang, Bai; Wu, Hanwen; Gao, Jie

Wang, Liugen; Wang, Yan; Xuan, Chenxu; Zhang, Bai; Wu, Hanwen; Gao, Jie.

Afiliación

Wang L; School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, Jiangsu 214122, China.
Wang Y; School of Science, Jiangnan University, Wuxi, Jiangsu 214122, China.
Xuan C; School of Science, Jiangnan University, Wuxi, Jiangsu 214122, China.
Zhang B; School of Science, Jiangnan University, Wuxi, Jiangsu 214122, China.
Wu H; School of Science, Jiangnan University, Wuxi, Jiangsu 214122, China.
Gao J; School of Science, Jiangnan University, Wuxi, Jiangsu 214122, China.

Brief Bioinform ; 24(4)2023 07 20.

Article en En | MEDLINE | ID: mdl-37406190

Asunto(s)

COVID-19; Aprendizaje Profundo; Microbiota; Humanos; Reproducibilidad de los Resultados; Algoritmos; Biología Computacional/métodos

Palabras clave

k-means clustering; deep sparse autoencoder neural network; microbedisease associations; random forest

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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Microbiota / Aprendizaje Profundo / COVID-19 Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Brief Bioinform Asunto de la revista: BIOLOGIA / INFORMATICA MEDICA Año: 2023 Tipo del documento: Article País de afiliación: China

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