{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T15:33:43Z","timestamp":1778686423017,"version":"3.51.4"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1009048","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2021,6,15]],"date-time":"2021-06-15T00:00:00Z","timestamp":1623715200000}}],"reference-count":33,"publisher":"Public Library of Science (PLoS)","issue":"6","license":[{"start":{"date-parts":[[2021,6,3]],"date-time":"2021-06-03T00:00:00Z","timestamp":1622678400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["11671009"],"award-info":[{"award-number":["11671009"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004731","name":"Natural Science Foundation of Zhejiang Province","doi-asserted-by":"publisher","award":["LZ19A010002"],"award-info":[{"award-number":["LZ19A010002"]}],"id":[{"id":"10.13039\/501100004731","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>Recently, an increasing number of studies have demonstrated that miRNAs are involved in human diseases, indicating that miRNAs might be a potential pathogenic factor for various diseases. Therefore, figuring out the relationship between miRNAs and diseases plays a critical role in not only the development of new drugs, but also the formulation of individualized diagnosis and treatment. As the prediction of miRNA-disease association via biological experiments is expensive and time-consuming, computational methods have a positive effect on revealing the association. In this study, a novel prediction model integrating GCN, CNN and Squeeze-and-Excitation Networks (GCSENet) was constructed for the identification of miRNA-disease association. The model first captured features by GCN based on a heterogeneous graph including diseases, genes and miRNAs. Then, considering the different effects of genes on each type of miRNA and disease, as well as the different effects of the miRNA-gene and disease-gene relationships on miRNA-disease association, a feature weight was set and a combination of miRNA-gene and disease-gene associations was added as feature input for the convolution operation in CNN. Furthermore, the squeeze and excitation blocks of SENet were applied to determine the importance of each feature channel and enhance useful features by means of the attention mechanism, thus achieving a satisfactory prediction of miRNA-disease association. The proposed method was compared against other state-of-the-art methods. It achieved an AUROC score of 95.02% and an AUPR score of 95.55% in a 10-fold cross-validation, which led to the finding that the proposed method is superior to these popular methods on most of the performance evaluation indexes.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1009048","type":"journal-article","created":{"date-parts":[[2021,6,3]],"date-time":"2021-06-03T18:16:55Z","timestamp":1622744215000},"page":"e1009048","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":22,"title":["GCSENet: A GCN, CNN and SENet ensemble model for microRNA-disease association 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Using information content to evaluate semantic similarity in a taxonomy. Proceedings of the 14th International Joint Conference on Artificial Intelligence; 1995 Aug; Montreal, Quebec, Canada; 1995. p. 448\u201353."},{"issue":"5","key":"pcbi.1009048.ref023","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1016\/j.ajhg.2008.09.017","article-title":"The Human Phenotype Ontology: a tool for annotating and analyzing human hereditary disease","volume":"83","author":"PN Robinson","year":"2008","journal-title":"Am J Hum Genet"},{"issue":"1","key":"pcbi.1009048.ref024","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1186\/1471-2105-15-248","article-title":"Clinical phenotype-based gene prioritization: an initial study using semantic similarity and the human phenotype ontology","volume":"15","author":"AJ Masino","year":"2014","journal-title":"BMC bioinformatics"},{"key":"pcbi.1009048.ref025","doi-asserted-by":"crossref","unstructured":"Ghorbani M, Baghshah MS, Rabiee HR. MGCN: Semi-supervised Classification in Multi-layer Graphs with Graph Convolutional Networks. Proceedings of the 2019 IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining; 2019 Aug; Vancouver, British Columbia, Canada; 2019. P. 208\u201311.","DOI":"10.1145\/3341161.3342942"},{"issue":"13","key":"pcbi.1009048.ref026","doi-asserted-by":"crossref","first-page":"i457","DOI":"10.1093\/bioinformatics\/bty294","article-title":"Modeling polypharmacy side effects with graph convolutional networks","volume":"34","author":"M Zitnik","year":"2018","journal-title":"Bioinformatics"},{"key":"pcbi.1009048.ref027","unstructured":"Nair V, Hinton GE. Rectified linear units improve restricted boltzmann machines. Proceedings of the 27th international conference on international conference on machine; 2010 Jun; Haifa, Israel; 2010. P. 807\u201314."},{"issue":"8","key":"pcbi.1009048.ref028","doi-asserted-by":"crossref","first-page":"2011","DOI":"10.1109\/TPAMI.2019.2913372","article-title":"Squeeze-and-Excitation Networks","volume":"42","author":"J Hu","year":"2020","journal-title":"Ieee T Pattern Anal"},{"issue":"1","key":"pcbi.1009048.ref029","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1186\/s13059-019-1811-3","article-title":"Benchmark of computational methods for predicting microRNA-disease associations","volume":"20","author":"Z Huang","year":"2019","journal-title":"Genome Biol"},{"issue":"7","key":"pcbi.1009048.ref030","doi-asserted-by":"crossref","first-page":"1145","DOI":"10.1016\/S0031-3203(96)00142-2","article-title":"The use of the area under the ROC curve in the evaluation of machine learning algorithms","volume":"30","author":"AP Bradley","year":"1997","journal-title":"Pattern Recognit"},{"key":"pcbi.1009048.ref031","doi-asserted-by":"crossref","unstructured":"Davis J, Goadrich M. The relationship between Precision-Recall and ROC curves. Proceedings of the 23rd International Conference on Machine Learning; 2006 Jun; Pittsburgh, Pennsylvania, USA; 2006. p. 233\u201340.","DOI":"10.1145\/1143844.1143874"},{"key":"pcbi.1009048.ref032","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, et al. Attention is all you need. Proceedings of the 31st International Conference on Neural Information Processing systems; 2017 Dec; Long Beach, California, USA; 2017. 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