{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,5]],"date-time":"2026-04-05T05:29:54Z","timestamp":1775366994092,"version":"3.50.1"},"reference-count":29,"publisher":"Oxford University Press (OUP)","issue":"14","license":[{"start":{"date-parts":[[2019,7,8]],"date-time":"2019-07-08T00:00:00Z","timestamp":1562544000000},"content-version":"vor","delay-in-days":7,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61732009"],"award-info":[{"award-number":["61732009"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61622213"],"award-info":[{"award-number":["61622213"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61772552"],"award-info":[{"award-number":["61772552"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61420106009"],"award-info":[{"award-number":["61420106009"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,7,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Computational drug repositioning is a cost-effective strategy to identify novel indications for existing drugs. Drug repositioning is often modeled as a recommendation system problem. Taking advantage of the known drug\u2013disease associations, the objective of the recommendation system is to identify new treatments by filling out the unknown entries in the drug\u2013disease association matrix, which is known as matrix completion. Underpinned by the fact that common molecular pathways contribute to many different diseases, the recommendation system assumes that the underlying latent factors determining drug\u2013disease associations are highly correlated. In other words, the drug\u2013disease matrix to be completed is low-rank. Accordingly, matrix completion algorithms efficiently constructing low-rank drug\u2013disease matrix approximations consistent with known associations can be of immense help in discovering the novel drug\u2013disease associations.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>In this article, we propose to use a bounded nuclear norm regularization (BNNR) method to complete the drug\u2013disease matrix under the low-rank assumption. Instead of strictly fitting the known elements, BNNR is designed to tolerate the noisy drug\u2013drug and disease\u2013disease similarities by incorporating a regularization term to balance the approximation error and the rank properties. Moreover, additional constraints are incorporated into BNNR to ensure that all predicted matrix entry values are within the specific interval. BNNR is carried out on an adjacency matrix of a heterogeneous drug\u2013disease network, which integrates the drug\u2013drug, drug\u2013disease and disease\u2013disease networks. It not only makes full use of available drugs, diseases and their association information, but also is capable of dealing with cold start naturally. Our computational results show that BNNR yields higher drug\u2013disease association prediction accuracy than the current state-of-the-art methods. The most significant gain is in prediction precision measured as the fraction of the positive predictions that are truly positive, which is particularly useful in drug design practice. Cases studies also confirm the accuracy and reliability of BNNR.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The code of BNNR is freely available at https:\/\/github.com\/BioinformaticsCSU\/BNNR.<\/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\/btz331","type":"journal-article","created":{"date-parts":[[2019,5,10]],"date-time":"2019-05-10T11:32:12Z","timestamp":1557487932000},"page":"i455-i463","source":"Crossref","is-referenced-by-count":168,"title":["Drug repositioning based on bounded nuclear norm regularization"],"prefix":"10.1093","volume":"35","author":[{"given":"Mengyun","family":"Yang","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Central South University, Changsha"},{"name":"Provincial Key Laboratory of Informational Service for Rural Area of Southwestern Hunan, Shaoyang University, Shaoyang, China"}]},{"given":"Huimin","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Central South University, Changsha"}]},{"given":"Yaohang","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Old Dominion University, Norfolk, VA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1516-0480","authenticated-orcid":false,"given":"Jianxin","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Central South University, Changsha"}]}],"member":"286","published-online":{"date-parts":[[2019,7,5]]},"reference":[{"key":"2023062712334766100_btz331-B1","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1093\/nar\/30.1.52","article-title":"Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders","volume":"30","author":"Ada","year":"2002","journal-title":"Nucleic Acids Res"},{"key":"2023062712334766100_btz331-B2","volume-title":"Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers","author":"Boyd","year":"2011"},{"key":"2023062712334766100_btz331-B3","doi-asserted-by":"crossref","first-page":"1956","DOI":"10.1137\/080738970","article-title":"A singular value thresholding algorithm for matrix completion","volume":"20","author":"Cai","year":"2010","journal-title":"SIAM J. 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