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However, the identification of drug\u2013disease associations through wet experiments is costly and inefficient. Hence, the development of efficient and high-accuracy computational methods for predicting drug\u2013disease associations is of great significance.<\/jats:p><jats:p>Results: In this paper, we propose a novel computational method named as layer attention graph convolutional network (LAGCN) for the drug\u2013disease association prediction. Specifically, LAGCN first integrates the known drug\u2013disease associations, drug\u2013drug similarities and disease\u2013disease similarities into a heterogeneous network, and applies the graph convolution operation to the network to learn the embeddings of drugs and diseases. Second, LAGCN combines the embeddings from multiple graph convolution layers using an attention mechanism. Third, the unobserved drug\u2013disease associations are scored based on the integrated embeddings. Evaluated by 5-fold cross-validations, LAGCN achieves an area under the precision\u2013recall curve of 0.3168 and an area under the receiver\u2013operating characteristic curve of 0.8750, which are better than the results of existing state-of-the-art prediction methods and baseline methods. The case study shows that LAGCN can discover novel associations that are not curated in our dataset.<\/jats:p><jats:p>Conclusion: LAGCN is a useful tool for predicting drug\u2013disease associations. This study reveals that embeddings from different convolution layers can reflect the proximities of different orders, and combining the embeddings by the attention mechanism can improve the prediction performances.<\/jats:p>","DOI":"10.1093\/bib\/bbaa243","type":"journal-article","created":{"date-parts":[[2020,9,1]],"date-time":"2020-09-01T11:09:48Z","timestamp":1598958588000},"source":"Crossref","is-referenced-by-count":371,"title":["Predicting drug\u2013disease associations through layer attention graph convolutional network"],"prefix":"10.1093","volume":"22","author":[{"given":"Zhouxin","family":"Yu","sequence":"first","affiliation":[{"name":"College of Informatics, Huazhong Agricultural University"}]},{"given":"Feng","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Informatics, Huazhong Agricultural University"}]},{"given":"Xiaohan","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Informatics, Huazhong Agricultural 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