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...

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Detalles Bibliográficos
Autores principales: Tan, Yaqin, Zou, Juan, Kuang, Linai, Wang, Xiangyi, Zeng, Bin, Zhang, Zhen, Wang, Lei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Research
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

Internet

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

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