RAMP: response-aware multi-task learning with contrastive regularization for cancer drug response prediction.

Lee, Kanggeun; Cho, Dongbin; Jang, Jinho; Choi, Kang; Jeong, Hyoung-Oh; Seo, Jiwon; Jeong, Won-Ki; Lee, Semin

Lee, Kanggeun; Cho, Dongbin; Jang, Jinho; Choi, Kang; Jeong, Hyoung-Oh; Seo, Jiwon; Jeong, Won-Ki; Lee, Semin.

Afiliação

Lee K; Department of Computer Science and Engineering at Korea University.
Cho D; Department of Computer Science at Hanyang University.
Jang J; Department of Biomedical Engineering at UNIST.
Choi K; Department of Computer Science at Hanyang University.
Jeong HO; Department of Biomedical Engineering at UNIST.
Seo J; Department of Computer Science at Hanyang University.
Jeong WK; Department of Computer Science and Engineering at Korea University.
Lee S; Department of Biomedical Engineering at UNIST.

Brief Bioinform ; 24(1)2023 01 19.

Article em En | MEDLINE | ID: mdl-36460623

Assuntos

Antineoplásicos; Neoplasias; Humanos; Teorema de Bayes; Algoritmos; Antineoplásicos/farmacologia; Antineoplásicos/uso terapêutico; Neoplasias/tratamento farmacológico; Neoplasias/genética; Redes Neurais de Computação

Palavras-chave

Bayesian neural network; contrastive learning; multiomics; multitask learning; network embedding

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Neoplasias / Antineoplásicos Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2023 Tipo de documento: Article

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