A model-agnostic framework to enhance knowledge graph-based drug combination prediction with drug-drug interaction data and supervised contrastive learning.

Gu, Jeonghyeon; Bang, Dongmin; Yi, Jungseob; Lee, Sangseon; Kim, Dong Kyu; Kim, Sun

Gu, Jeonghyeon; Bang, Dongmin; Yi, Jungseob; Lee, Sangseon; Kim, Dong Kyu; Kim, Sun.

Afiliação

Gu J; Interdisciplinary Program in Artificial Intelligence, Seoul National University, 1, Gwanak-ro, 08826 Seoul, Republic of Korea.
Bang D; Interdisciplinary Program in Bioinformatics, Seoul National University, 1, Gwanak-ro, 08826 Seoul, Republic of Korea.
Yi J; AIGENDRUG Co., Ltd., 1, Gwanak-ro, 08826 Seoul, Republic of Korea.
Lee S; Interdisciplinary Program in Artificial Intelligence, Seoul National University, 1, Gwanak-ro, 08826 Seoul, Republic of Korea.
Kim DK; Institute of Computer Technology Seoul National University, 1, Gwanak-ro, 08826 Seoul, Republic of Korea.
Kim S; PHARMGENSCIENCE Co., Ltd., 216, Dongjak-daero, 06554 Seoul, Republic of Korea.

Brief Bioinform ; 24(5)2023 09 20.

Article em En | MEDLINE | ID: mdl-37544660

Assuntos

Algoritmos; Reconhecimento Automatizado de Padrão; Benchmarking; Combinação de Medicamentos; Interações Medicamentosas

Palavras-chave

drug combination; drugdrug interaction; graph neural network; knowledge graph; random walk; supervised contrastive learning

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Algoritmos / Reconhecimento Automatizado de Padrão Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Brief Bioinform Ano de publicação: 2023 Tipo de documento: Article

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