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It\u2019s unfeasible to identify all potential DDIs using experimental methods which are time-consuming and expensive. Computational methods provide an effective strategy, however, facing challenges due to the lack of experimentally verified negative samples.<\/jats:p>\n<\/jats:sec><jats:sec>\n<jats:title>Results<\/jats:title>\n<jats:p>To address this problem, we propose a novel positive-unlabeled learning method named DDI-PULearn for large-scale drug-drug-interaction predictions. DDI-PULearn first generates seeds of reliable negatives via OCSVM (one-class support vector machine) under a high-recall constraint and via the cosine-similarity based KNN (k-nearest neighbors) as well. Then trained with all the labeled positives (i.e., the validated DDIs) and the generated seed negatives, DDI-PULearn employs an iterative SVM to identify a set of entire reliable negatives from the unlabeled samples (i.e., the unobserved DDIs). Following that, DDI-PULearn represents all the labeled positives and the identified negatives as vectors of abundant drug properties by a similarity-based method. Finally, DDI-PULearn transforms these vectors into a lower-dimensional space via PCA (principal component analysis) and utilizes the compressed vectors as input for binary classifications. The performance of DDI-PULearn is evaluated on simulative prediction for 149,878 possible interactions between 548 drugs, comparing with two baseline methods and five state-of-the-art methods. Related experiment results show that the proposed method for the representation of DDIs characterizes them accurately. DDI-PULearn achieves superior performance owing to the identified reliable negatives, outperforming all other methods significantly. In addition, the predicted novel DDIs suggest that DDI-PULearn is capable to identify novel DDIs.<\/jats:p>\n<\/jats:sec><jats:sec>\n<jats:title>Conclusions<\/jats:title>\n<jats:p>The results demonstrate that positive-unlabeled learning paves a new way to tackle the problem caused by the lack of experimentally verified negatives in the computational prediction of DDIs.<\/jats:p>\n<\/jats:sec>","DOI":"10.1186\/s12859-019-3214-6","type":"journal-article","created":{"date-parts":[[2019,12,24]],"date-time":"2019-12-24T02:02:43Z","timestamp":1577152963000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":42,"title":["DDI-PULearn: a positive-unlabeled learning method for large-scale prediction of drug-drug interactions"],"prefix":"10.1186","volume":"20","author":[{"given":"Yi","family":"Zheng","sequence":"first","affiliation":[]},{"given":"Hui","family":"Peng","sequence":"additional","affiliation":[]},{"given":"Xiaocai","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Zhixun","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Xiaoying","family":"Gao","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1833-7413","authenticated-orcid":false,"given":"Jinyan","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,12,24]]},"reference":[{"issue":"1","key":"3214_CR1","doi-asserted-by":"publisher","first-page":"144","DOI":"10.1111\/j.1365-2125.2007.02981.x","volume":"65","author":"J Strandell","year":"2008","unstructured":"Strandell J, Bate A, Lindquist M, Edwards IR, Swedish IX-rd-didtSg. 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