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However, at present, few computational methods for predicting microbe\u2013drug associations have been proposed.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>In this work, we proposed a novel computational model named GSAMDA based on the graph attention network and sparse autoencoder to infer latent microbe\u2013drug associations. In GSAMDA, we first built a heterogeneous network through integrating known microbe\u2013drug associations, microbe similarities and drug similarities. And then, we adopted a GAT-based autoencoder and a sparse autoencoder module respectively to learn topological representations and attribute representations for nodes in the newly constructed heterogeneous network. Finally, based on these two kinds of node representations, we constructed two kinds of feature matrices for microbes and drugs separately, and then, utilized them to calculate possible association scores for microbe\u2013drug pairs.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>A novel computational model is proposed for predicting potential microbe\u2013drug associations based on graph attention network and sparse autoencoder. Compared with other five state-of-the-art competitive methods, the experimental results illustrated that our model can achieve better performance. Moreover, case studies on two categories of representative drugs and microbes further demonstrated the effectiveness of our model as well.<\/jats:p><\/jats:sec>","DOI":"10.1186\/s12859-022-05053-7","type":"journal-article","created":{"date-parts":[[2022,11,18]],"date-time":"2022-11-18T15:04:37Z","timestamp":1668783877000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":41,"title":["GSAMDA: a computational model for predicting potential microbe\u2013drug associations based on graph attention network and sparse autoencoder"],"prefix":"10.1186","volume":"23","author":[{"given":"Yaqin","family":"Tan","sequence":"first","affiliation":[]},{"given":"Juan","family":"Zou","sequence":"additional","affiliation":[]},{"given":"Linai","family":"Kuang","sequence":"additional","affiliation":[]},{"given":"Xiangyi","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Bin","family":"Zeng","sequence":"additional","affiliation":[]},{"given":"Zhen","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Lei","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,11,18]]},"reference":[{"issue":"7402","key":"5053_CR1","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1038\/nature11234","volume":"486","author":"C Huttenhower","year":"2012","unstructured":"Huttenhower C, Gevers D, Knight R, et al. 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