BayeshERG: a robust, reliable and interpretable deep learning model for predicting hERG channel blockers.

Kim, Hyunho; Park, Minsu; Lee, Ingoo; Nam, Hojung

Kim, Hyunho; Park, Minsu; Lee, Ingoo; Nam, Hojung.

Afiliação

Kim H; School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Buk-gu, Gwangju, 61005, Republic of Korea.
Park M; School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Buk-gu, Gwangju, 61005, Republic of Korea.
Lee I; School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Buk-gu, Gwangju, 61005, Republic of Korea.
Nam H; School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Buk-gu, Gwangju, 61005, Republic of Korea.

Brief Bioinform ; 23(4)2022 07 18.

Article em En | MEDLINE | ID: mdl-35709752

Assuntos

Aprendizado Profundo; Canais de Potássio Éter-A-Go-Go; Teorema de Bayes; Canais de Potássio Éter-A-Go-Go/química; Canais de Potássio Éter-A-Go-Go/genética; Humanos; Bloqueadores dos Canais de Potássio/química; Bloqueadores dos Canais de Potássio/farmacologia; Reprodutibilidade dos Testes

Palavras-chave

Attention mechanism; Bayesian deep learning; Graph neural network; Transfer learning; hERG prediction

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Canais de Potássio Éter-A-Go-Go / Aprendizado Profundo Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: Brief Bioinform Assunto da revista: BIOLOGIA / INFORMATICA MEDICA Ano de publicação: 2022 Tipo de documento: Article

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