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Given the highly cell-specific nature of miRNA regulation, recent computational approaches typically exploit expression data to identify the most physiologically relevant target messenger RNAs (mRNAs). Although effective, those methods usually require a large sample size to infer miRNA\u2013mRNA interactions, thus limiting their applications in personalized medicine. In this study, we developed a novel miRNA target prediction algorithm called miRACLe (miRNA Analysis by a Contact modeL). It integrates sequence characteristics and RNA expression profiles into a random contact model, and determines the target preferences by relative probability of effective contacts in an individual-specific manner. Evaluation by a variety of measures shows that fitting TargetScan, a frequently used prediction tool, into the framework of miRACLe can improve its predictive power with a significant margin and consistently outperform other state-of-the-art methods in prediction accuracy, regulatory potential and biological relevance. Notably, the superiority of miRACLe is robust to various biological contexts, types of expression data and validation datasets, and the computation process is fast and efficient. Additionally, we show that the model can be readily applied to other sequence-based algorithms to improve their predictive power, such as DIANA-microT-CDS, miRanda-mirSVR and MirTarget4. MiRACLe is publicly available at https:\/\/github.com\/PANWANG2014\/miRACLe.<\/jats:p>","DOI":"10.1093\/bib\/bbaa117","type":"journal-article","created":{"date-parts":[[2020,5,19]],"date-time":"2020-05-19T11:39:14Z","timestamp":1589888354000},"source":"Crossref","is-referenced-by-count":2,"title":["MiRACLe: an individual-specific approach to improve microRNA-target prediction based on a random contact model"],"prefix":"10.1093","volume":"22","author":[{"given":"Pan","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Thoracic Surgery, National Cancer Center\/National Clinical Research Center for Cancer\/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qi","family":"Li","sequence":"additional","affiliation":[{"name":"Center for Statistical Science & Department of Industry Engineering, Tsinghua University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nan","family":"Sun","sequence":"additional","affiliation":[{"name":"Department of Thoracic Surgery, National Cancer Center\/National Clinical Research Center for Cancer\/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yibo","family":"Gao","sequence":"additional","affiliation":[{"name":"Department of Thoracic Surgery, National Cancer Center\/National Clinical Research Center for Cancer\/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun S","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Statistics, Harvard University, Cambridge, MA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ke","family":"Deng","sequence":"additional","affiliation":[{"name":"Center for Statistical Science & Department of Industry Engineering, Tsinghua University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"He","sequence":"additional","affiliation":[{"name":"Department of Thoracic Surgery, National Cancer Center\/National Clinical Research Center for Cancer\/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2020,7,7]]},"reference":[{"key":"2021052110033449400_ref1","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.cell.2018.03.006","article-title":"Metazoan 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