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Recently, biological network-based approaches have been proven to be effective in predicting chemical-gene interactions.\n<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>We present CGINet, a graph convolutional network-based method for identifying chemical-gene interactions in an integrated multi-relational graph containing three types of nodes: chemicals, genes, and pathways. We investigate two different perspectives on learning node embeddings. One is to view the graph as a whole, and the other is to adopt a subgraph view that initial node embeddings are learned from the binary association subgraphs and then transferred to the multi-interaction subgraph for more focused learning of higher-level target node representations. Besides, we reconstruct the topological structures of target nodes with the latent links captured by the designed substructures. CGINet adopts an end-to-end way that the encoder and the decoder are trained jointly with known chemical-gene interactions. We aim to predict unknown but potential associations between chemicals and genes as well as their interaction types.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>We study three model implementations CGINet-1\/2\/3 with various components and compare them with baseline approaches. As the experimental results suggest, our models exhibit competitive performances on identifying chemical-gene interactions. Besides, the subgraph perspective and the latent link both play positive roles in learning much more informative node embeddings and can lead to improved prediction.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12859-020-03899-3","type":"journal-article","created":{"date-parts":[[2020,11,26]],"date-time":"2020-11-26T12:03:40Z","timestamp":1606392220000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["CGINet: graph convolutional network-based model for identifying chemical-gene interaction in an integrated multi-relational graph"],"prefix":"10.1186","volume":"21","author":[{"given":"Wei","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xi","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9688-5311","authenticated-orcid":false,"given":"Chengkun","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Canqun","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,11,26]]},"reference":[{"issue":"3","key":"3899_CR1","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1038\/nrd3078","volume":"9","author":"SM Paul","year":"2010","unstructured":"Paul SM, Mytelka DS, Dunwiddie CT, et al. 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