Prediction of microbe–drug associations based on a modified graph attention variational autoencoder and random forest

The identification of microbe-drug associations can greatly facilitate drug research and development. Traditional methods for screening microbe-drug associations are time-consuming, manpower-intensive, and costly to conduct, so computational methods are a good alternative. However, most of them igno...

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Veröffentlicht in:	Frontiers in microbiology Jg. 15; S. 1394302
Hauptverfasser:	Wang, Bo, Ma, Fangjian, Du, Xiaoxin, Zhang, Guangda, Li, Jingyou
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Switzerland Frontiers Media S.A 31.05.2024
Schlagworte:	computational prediction model graph attention network microbe–drug association prediction Microbiology modified graph convolutional neural network random forest classifier variational autoencoder graph attention network computational prediction model microbe–drug association prediction random forest classifier variational autoencoder modified graph convolutional neural network
ISSN:	1664-302X, 1664-302X
Online-Zugang:	Volltext
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