{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T15:19:55Z","timestamp":1773674395508,"version":"3.50.1"},"reference-count":33,"publisher":"Oxford University Press (OUP)","issue":"15","license":[{"start":{"date-parts":[[2017,4,18]],"date-time":"2017-04-18T00:00:00Z","timestamp":1492473600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/about_us\/legal\/notices"}],"funder":[{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","award":["U24AI117966"],"award-info":[{"award-number":["U24AI117966"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","award":["P30CA023100"],"award-info":[{"award-number":["P30CA023100"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017,8,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>A heterogeneous network topology possessing abundant interactions between biomedical entities has yet to be utilized in similarity-based methods for predicting drug\u2013target associations based on the array of varying features of drugs and their targets. Deep learning reveals features of vertices of a large network that can be adapted in accommodating the similarity-based solutions to provide a flexible method of drug\u2013target prediction.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We propose a similarity-based drug\u2013target prediction method that enhances existing association discovery methods by using a topology-based similarity measure. DeepWalk, a deep learning method, is adopted in this study to calculate the similarities within Linked Tripartite Network (LTN), a heterogeneous network generated from biomedical linked datasets. This proposed method shows promising results for drug\u2013target association prediction: 98.96% AUC ROC score with a 10-fold cross-validation and 99.25% AUC ROC score with a Monte Carlo cross-validation with LTN. By utilizing DeepWalk, we demonstrate that: (i) this method outperforms other existing topology-based similarity computation methods, (ii) the performance is better for tripartite than with bipartite networks and (iii) the measure of similarity using network topology outperforms the ones derived from chemical structure (drugs) or genomic sequence (targets). Our proposed methodology proves to be capable of providing a promising solution for drug\u2013target prediction based on topological similarity with a heterogeneous network, and may be readily re-purposed and adapted in the existing of similarity-based methodologies.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and Implementation<\/jats:title>\n                  <jats:p>The proposed method has been developed in JAVA and it is available, along with the data at the following URL: https:\/\/github.com\/zongnansu1982\/drug-target-prediction.<\/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\/btx160","type":"journal-article","created":{"date-parts":[[2017,3,22]],"date-time":"2017-03-22T00:55:07Z","timestamp":1490144107000},"page":"2337-2344","source":"Crossref","is-referenced-by-count":169,"title":["Deep mining heterogeneous networks of biomedical linked data to predict novel drug\u2013target associations"],"prefix":"10.1093","volume":"33","author":[{"given":"Nansu","family":"Zong","sequence":"first","affiliation":[{"name":"Department of Biomedical Informatics, School of Medicine, UC, San Diego, CA, USA"}]},{"given":"Hyeoneui","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, School of Medicine, UC, San Diego, CA, USA"}]},{"given":"Victoria","family":"Ngo","sequence":"additional","affiliation":[{"name":"Betty Irene Moore School of Nursing, UC Davis, Sacramento, CA, USA"}]},{"given":"Olivier","family":"Harismendy","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, School of Medicine, UC, San Diego, CA, USA"},{"name":"Moores Cancer Center, UC, San Diego, CA, USA"}]}],"member":"286","published-online":{"date-parts":[[2017,4,18]]},"reference":[{"key":"2023063012484660100_btx160-B1","doi-asserted-by":"crossref","first-page":"1169","DOI":"10.1038\/nmeth.2728","article-title":"Using networks to measure similarity between genes: association index selection","volume":"10","author":"Bass","year":"2013","journal-title":"Nat. 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