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Thus, identifying new uses for approved drugs, named drug repositioning, is helpful for speeding up the drug development process and reducing development costs. Existing drug-related disease prediction methods mainly focus on single or multiple drug\u2013disease heterogeneous networks. However, heterogeneous networks, and drug subnets and disease subnet contained in heterogeneous networks cover the common topology information between drug and disease nodes, the specific information between drug nodes and the specific information between disease nodes, respectively.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>We design a novel model, CTST, to extract and integrate common and specific topologies in multiple heterogeneous networks and subnets. Multiple heterogeneous networks composed of drug and disease nodes are established to integrate multiple kinds of similarities and associations among drug and disease nodes. These heterogeneous networks contain multiple drug subnets and a disease subnet. For multiple heterogeneous networks and subnets, we then define the common and specific representations of drug and disease nodes. The common representations of drug and disease nodes are encoded by a graph convolutional autoencoder with sharing parameters and they integrate the topological relationships of all nodes in heterogeneous networks. The specific representations of nodes are learned by specific graph convolutional autoencoders, respectively, and they fuse the topology and attributes of the nodes in each subnet. We then propose attention mechanisms at common representation level and specific representation level to learn more informative common and specific representations, respectively. Finally, an integration module with representation feature level attention is built to adaptively integrate these two representations for final association prediction. Extensive experimental results confirm the effectiveness of CTST. Comparison with six latest methods and case studies on five drugs further verify CTST has the ability to discover potential candidate diseases.<\/jats:p><\/jats:sec>","DOI":"10.1093\/bib\/bbab467","type":"journal-article","created":{"date-parts":[[2021,10,13]],"date-time":"2021-10-13T13:48:34Z","timestamp":1634132914000},"source":"Crossref","is-referenced-by-count":13,"title":["Prediction of drug\u2013disease associations by integrating common topologies of heterogeneous networks and specific topologies of subnets"],"prefix":"10.1093","volume":"23","author":[{"given":"Ling","family":"Gao","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China"}]},{"given":"Hui","family":"Cui","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Technology, La Trobe University, Melbourne 3083, Australia"}]},{"given":"Tiangang","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Mathematical Science, Heilongjiang University, Harbin 150080, China"}]},{"given":"Nan","family":"Sheng","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Jilin University, Changchun 130012, China"}]},{"given":"Ping","family":"Xuan","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China"}]}],"member":"286","published-online":{"date-parts":[[2021,11,30]]},"reference":[{"issue":"2","key":"2022012000295893400_ref1","doi-asserted-by":"crossref","first-page":"2058","DOI":"10.1093\/bib\/bbaa027","article-title":"Drug repositioning by prediction of drug\u2019s anatomical therapeutic chemical code via network-based inference approaches","volume":"22","author":"Peng","year":"2020","journal-title":"Brief Bioinform"},{"issue":"2","key":"2022012000295893400_ref2","doi-asserted-by":"crossref","first-page":"153","DOI":"10.4103\/0022-3859.81870","article-title":"Drug repositioning: re-investigating existing drugs for new therapeutic indications","volume":"57","author":"Padhy","year":"2011","journal-title":"J Postgrad Med"},{"issue":"2","key":"2022012000295893400_ref3","doi-asserted-by":"crossref","first-page":"1604","DOI":"10.1093\/bib\/bbz176","article-title":"Biomedical data and computational models for drug repositioning: a comprehensive review","volume":"22","author":"Luo","year":"2021","journal-title":"Brief Bioinform"},{"issue":"3","key":"2022012000295893400_ref4","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1002\/mds.27604","article-title":"I Cond, et al. 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