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However, developing such computational methods is not an easy task, but is much needed, as current methods that predict potential drug\u2013target interactions suffer from high false-positive rates. Here we introduce DTiGEMS+, a computational method that predicts<jats:underline>D<\/jats:underline>rug\u2013<jats:underline>T<\/jats:underline>arget<jats:underline>i<\/jats:underline>nteractions using<jats:underline>G<\/jats:underline>raph<jats:underline>E<\/jats:underline>mbedding, graph<jats:underline>M<\/jats:underline>ining, and<jats:underline>S<\/jats:underline>imilarity-based techniques. DTiGEMS+\u2009combines similarity-based as well as feature-based approaches, and models the identification of novel drug\u2013target interactions as a link prediction problem in a heterogeneous network. DTiGEMS+\u2009constructs the heterogeneous network by augmenting the known drug\u2013target interactions graph with two other complementary graphs namely: drug\u2013drug similarity, target\u2013target similarity. DTiGEMS+\u2009combines different computational techniques to provide the final drug target prediction, these techniques include graph embeddings, graph mining, and machine learning. DTiGEMS+\u2009integrates multiple drug\u2013drug similarities and target\u2013target similarities into the final heterogeneous graph construction after applying a similarity selection procedure as well as a similarity fusion algorithm. Using four benchmark datasets, we show DTiGEMS+\u2009substantially improves prediction performance compared to other state-of-the-art in silico methods developed to predict of drug-target interactions by achieving the highest average AUPR across all datasets (0.92), which reduces the error rate by 33.3% relative to the second-best performing model in the state-of-the-art methods comparison.<\/jats:p>","DOI":"10.1186\/s13321-020-00447-2","type":"journal-article","created":{"date-parts":[[2020,6,29]],"date-time":"2020-06-29T12:04:05Z","timestamp":1593432245000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":113,"title":["DTiGEMS+: drug\u2013target interaction prediction using graph embedding, graph mining, and similarity-based techniques"],"prefix":"10.1186","volume":"12","author":[{"given":"Maha A.","family":"Thafar","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rawan S.","family":"Olayan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haitham","family":"Ashoor","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Somayah","family":"Albaradei","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vladimir B.","family":"Bajic","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Gao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Takashi","family":"Gojobori","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2709-5356","authenticated-orcid":false,"given":"Magbubah","family":"Essack","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,6,29]]},"reference":[{"issue":"2","key":"447_CR1","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1016\/S0167-6296(02)00126-1","volume":"22","author":"JA DiMasi","year":"2003","unstructured":"DiMasi JA, Hansen RW, Grabowski HG (2003) The price of innovation: new estimates of drug development costs. 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