{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,5]],"date-time":"2026-04-05T05:29:50Z","timestamp":1775366990746,"version":"3.50.1"},"reference-count":18,"publisher":"Springer Science and Business Media LLC","issue":"S2","license":[{"start":{"date-parts":[[2010,9,1]],"date-time":"2010-09-01T00:00:00Z","timestamp":1283299200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/2.0"},{"start":{"date-parts":[[2010,9,13]],"date-time":"2010-09-13T00:00:00Z","timestamp":1284336000000},"content-version":"vor","delay-in-days":12,"URL":"https:\/\/creativecommons.org\/licenses\/by\/2.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Syst Biol"],"published-print":{"date-parts":[[2010,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:sec>\n            <jats:title>Background<\/jats:title>\n            <jats:p>Predicting drug-protein interactions from heterogeneous biological data sources is a key step for <jats:italic>in silico<\/jats:italic> drug discovery. The difficulty of this prediction task lies in the rarity of known drug-protein interactions and myriad unknown interactions to be predicted. To meet this challenge, a manifold regularization semi-supervised learning method is presented to tackle this issue by using labeled and unlabeled information which often generates better results than using the labeled data alone. Furthermore, our semi-supervised learning method integrates known drug-protein interaction network information as well as chemical structure and genomic sequence data.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>Using the proposed method, we predicted certain drug-protein interactions on the enzyme, ion channel, GPCRs, and nuclear receptor data sets. Some of them are confirmed by the latest publicly available drug targets databases such as KEGG.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusions<\/jats:title>\n            <jats:p>We report encouraging results of using our method for drug-protein interaction network reconstruction which may shed light on the molecular interaction inference and new uses of marketed drugs.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1186\/1752-0509-4-s2-s6","type":"journal-article","created":{"date-parts":[[2010,9,14]],"date-time":"2010-09-14T06:15:35Z","timestamp":1284444935000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":296,"title":["Semi-supervised drug-protein interaction prediction from heterogeneous biological spaces"],"prefix":"10.1186","volume":"4","author":[{"given":"Zheng","family":"Xia","sequence":"first","affiliation":[]},{"given":"Ling-Yun","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Xiaobo","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Stephen TC","family":"Wong","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2010,9,13]]},"reference":[{"issue":"2","key":"536_CR1","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1101\/gr.6888208","volume":"18","author":"L Yao","year":"2008","unstructured":"Yao L, Rzhetsky A: Quantitative systems-level determinants of human genes targeted by successful drugs. 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