SPARSE: a sparse hypergraph neural network for learning multiple types of latent combinations to accurately predict drug–drug interactions

MOTIVATION: Predicting side effects of drug–drug interactions (DDIs) is an important task in pharmacology. The state-of-the-art methods for DDI prediction use hypergraph neural networks to learn latent representations of drugs and side effects to express high-order relationships among two interactin...

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Detalles Bibliográficos
Autores principales: Nguyen, Duc Anh, Nguyen, Canh Hao, Petschner, Peter, Mamitsuka, Hiroshi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
ISCB/Ismb 2022
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9235485/
https://www.ncbi.nlm.nih.gov/pubmed/35758803
http://dx.doi.org/10.1093/bioinformatics/btac250

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9235485/
https://www.ncbi.nlm.nih.gov/pubmed/35758803
http://dx.doi.org/10.1093/bioinformatics/btac250

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