{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T19:23:15Z","timestamp":1783970595667,"version":"3.55.0"},"reference-count":48,"publisher":"Oxford University Press (OUP)","issue":"9","license":[{"start":{"date-parts":[[2017,12,8]],"date-time":"2017-12-08T00:00:00Z","timestamp":1512691200000},"content-version":"vor","delay-in-days":1,"URL":"https:\/\/academic.oup.com\/journals\/pages\/about_us\/legal\/notices"}],"funder":[{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61402378"],"award-info":[{"award-number":["61402378"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,5,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Motivation<\/jats:title><jats:p>Long non-coding RNAs (lncRNAs) play crucial roles in complex disease diagnosis, prognosis, prevention and treatment, but only a small portion of lncRNA\u2013disease associations have been experimentally verified. Various computational models have been proposed to identify lncRNA\u2013disease associations by integrating heterogeneous data sources. However, existing models generally ignore the intrinsic structure of data sources or treat them as equally relevant, while they may not be.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>To accurately identify lncRNA\u2013disease associations, we propose a Matrix Factorization based LncRNA\u2013Disease Association prediction model (MFLDA in short). MFLDA decomposes data matrices of heterogeneous data sources into low-rank matrices via matrix tri-factorization to explore and exploit their intrinsic and shared structure. MFLDA can select and integrate the data sources by assigning different weights to them. An iterative solution is further introduced to simultaneously optimize the weights and low-rank matrices. Next, MFLDA uses the optimized low-rank matrices to reconstruct the lncRNA\u2013disease association matrix and thus to identify potential associations. In 5-fold cross validation experiments to identify verified lncRNA\u2013disease associations, MFLDA achieves an area under the receiver operating characteristic curve (AUC) of 0.7408, at least 3% higher than those given by state-of-the-art data fusion based computational models. An empirical study on identifying masked lncRNA\u2013disease associations again shows that MFLDA can identify potential associations more accurately than competing models. A case study on identifying lncRNAs associated with breast, lung and stomach cancers show that 38 out of 45 (84%) associations predicted by MFLDA are supported by recent biomedical literature and further proves the capability of MFLDA in identifying novel lncRNA\u2013disease associations. MFLDA is a general data fusion framework, and as such it can be adopted to predict associations between other biological entities.<\/jats:p><\/jats:sec><jats:sec><jats:title>Availability and implementation<\/jats:title><jats:p>The source code for MFLDA is available at: http:\/\/mlda.swu.edu.cn\/codes.php? name = MFLDA.<\/jats:p><\/jats:sec><jats:sec><jats:title>Supplementary information<\/jats:title><jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p><\/jats:sec>","DOI":"10.1093\/bioinformatics\/btx794","type":"journal-article","created":{"date-parts":[[2017,12,5]],"date-time":"2017-12-05T20:16:47Z","timestamp":1512505007000},"page":"1529-1537","source":"Crossref","is-referenced-by-count":207,"title":["Matrix factorization-based data fusion for the prediction of lncRNA\u2013disease associations"],"prefix":"10.1093","volume":"34","author":[{"given":"Guangyuan","family":"Fu","sequence":"first","affiliation":[{"name":"College of Computer and Information Science, Southwest University, Chongqing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5890-0365","authenticated-orcid":false,"given":"Jun","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Computer and Information Science, Southwest University, Chongqing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carlotta","family":"Domeniconi","sequence":"additional","affiliation":[{"name":"Department of Computer Science, George Mason University, Farifax, VA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1667-6705","authenticated-orcid":false,"given":"Guoxian","family":"Yu","sequence":"additional","affiliation":[{"name":"College of Computer and Information Science, Southwest University, Chongqing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2017,12,7]]},"reference":[{"key":"2023012713021518500_btx794-B1","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1038\/75556","article-title":"Gene Ontology: tool for the unification of biology","volume":"25","author":"Ashburner","year":"2000","journal-title":"Nat. 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