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GSAMDA: a computational model for predicting potential microbe–drug associations based on graph attention network and sparse autoencoder
BACKGROUND: Clinical studies show that microorganisms are closely related to human health, and the discovery of potential associations between microbes and drugs will facilitate drug research and development. However, at present, few computational methods for predicting microbe–drug associations hav...
| Autores principales: | , , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
BioMed Central
2022
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9673879/ https://www.ncbi.nlm.nih.gov/pubmed/36401174 http://dx.doi.org/10.1186/s12859-022-05053-7 |