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Computational approaches for DTI prediction that adopt the systems biology perspective generally exploit the rationale that the properties of drugs and targets can be characterized by their functional roles in biological networks.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Inspired by recent advance of information passing and aggregation techniques that generalize the convolution neural networks to mine large-scale graph data and greatly improve the performance of many network-related prediction tasks, we develop a new nonlinear end-to-end learning model, called NeoDTI, that integrates diverse information from heterogeneous network data and automatically learns topology-preserving representations of drugs and targets to facilitate DTI prediction. The substantial prediction performance improvement over other state-of-the-art DTI prediction methods as well as several novel predicted DTIs with evidence supports from previous studies have demonstrated the superior predictive power of NeoDTI. In addition, NeoDTI is robust against a wide range of choices of hyperparameters and is ready to integrate more drug and target related information (e.g. compound\u2013protein binding affinity data). All these results suggest that NeoDTI can offer a powerful and robust tool for drug development and drug repositioning.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>The source code and data used in NeoDTI are available at: https:\/\/github.com\/FangpingWan\/NeoDTI.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Supplementary information<\/jats:title>\n                    <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/bty543","type":"journal-article","created":{"date-parts":[[2018,6,29]],"date-time":"2018-06-29T08:11:58Z","timestamp":1530259918000},"page":"104-111","source":"Crossref","is-referenced-by-count":331,"title":["NeoDTI: neural integration of neighbor information from a heterogeneous network for discovering new drug\u2013target interactions"],"prefix":"10.1093","volume":"35","author":[{"given":"Fangping","family":"Wan","sequence":"first","affiliation":[{"name":"Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China"}]},{"given":"Lixiang","family":"Hong","sequence":"additional","affiliation":[{"name":"Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China"}]},{"given":"An","family":"Xiao","sequence":"additional","affiliation":[{"name":"Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China"}]},{"given":"Tao","family":"Jiang","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, Tsinghua University, Beijing, China"},{"name":"Bioinformatics Division, BNRIST, Tsinghua University, Beijing, China"},{"name":"Department of Computer Science and Engineering, University of California, Riverside, CA, USA"}]},{"given":"Jianyang","family":"Zeng","sequence":"additional","affiliation":[{"name":"Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China"}]}],"member":"286","published-online":{"date-parts":[[2018,7,2]]},"reference":[{"key":"2023013107222939400_bty543-B1","doi-asserted-by":"crossref","first-page":"2004","DOI":"10.1093\/bioinformatics\/btt307","article-title":"Drug\u2013target interaction prediction through domain-tuned network-based inference","volume":"29","author":"Alaimo","year":"2013","journal-title":"Bioinformatics"},{"key":"2023013107222939400_bty543-B2","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1021\/acscentsci.6b00367","article-title":"Low data drug discovery with one-shot learning","volume":"3","author":"Altae-Tran","year":"2017","journal-title":"ACS Cent. 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