{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T02:20:56Z","timestamp":1742955656678,"version":"3.40.3"},"publisher-location":"Cham","reference-count":32,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030845315"},{"type":"electronic","value":"9783030845322"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-84532-2_54","type":"book-chapter","created":{"date-parts":[[2021,8,8]],"date-time":"2021-08-08T23:04:24Z","timestamp":1628463864000},"page":"603-614","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An Efficient Computational Method to Predict Drug-Target Interactions Utilizing Matrix Completion and Linear Optimization Method"],"prefix":"10.1007","author":[{"given":"Xinguo","family":"Lu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinxin","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Keren","family":"He","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kaibao","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Changlong","family":"Gu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,8,9]]},"reference":[{"doi-asserted-by":"crossref","unstructured":"Olayan, R.S., Ashoor, H., Bajic, V.B.: DDR: efficient computational method to predict drug\u2013target interactions using graph mining and machine learning approaches. Bioinformatics 34(7), 1164\u20131173 (2017)","key":"54_CR1","DOI":"10.1093\/bioinformatics\/btx731"},{"issue":"21","key":"54_CR2","doi-asserted-by":"publisher","first-page":"8685","DOI":"10.1073\/pnas.0701361104","volume":"104","author":"KI Goh","year":"2007","unstructured":"Goh, K.I., Cusick, M.E., Valle, D., et al.: The human disease network. Proc. Natl. Acad. Sci. 104(21), 8685\u20138690 (2007)","journal-title":"Proc. Natl. Acad. Sci."},{"doi-asserted-by":"crossref","unstructured":"Avorn, J.: The $2.6 billion pill--methodologic and policy considerations. New Engl. J. Med. 372(20), 1877\u20131879 (2015)","key":"54_CR3","DOI":"10.1056\/NEJMp1500848"},{"key":"54_CR4","doi-asserted-by":"publisher","first-page":"40376","DOI":"10.1038\/srep40376","volume":"7","author":"H Ming","year":"2017","unstructured":"Ming, H., Bryant, S.H., Wang, Y.: Predicting drug-target interactions by dual-network integrated logistic matrix factorization. Sci. Rep. 7, 40376 (2017)","journal-title":"Sci. Rep."},{"doi-asserted-by":"crossref","unstructured":"Malina, D., Greene, J.A., Loscalzo, J.: Putting the patient back together - social medicine, network medicine, and the limits of reductionism. New Engl. J. Med. 377(25), 2493 (2017)","key":"54_CR5","DOI":"10.1056\/NEJMms1706744"},{"issue":"7","key":"54_CR6","doi-asserted-by":"publisher","first-page":"2251","DOI":"10.1007\/s00894-014-2251-3","volume":"20","author":"L Guo","year":"2014","unstructured":"Guo, L., Yan, Z., Zheng, X., et al.: A comparison of various optimization algorithms of protein\u2013ligand docking programs by fitness accuracy. J. Mol. Model. 20(7), 2251 (2014). https:\/\/doi.org\/10.1007\/s00894-014-2251-3","journal-title":"J. Mol. Model."},{"doi-asserted-by":"crossref","unstructured":"Liu, Y., et al.: Neighborhood regularized logistic matrix factorization for drug-target interaction prediction. PLoS Comput. Biol. 12 (2016)","key":"54_CR7","DOI":"10.1371\/journal.pcbi.1004760"},{"doi-asserted-by":"crossref","unstructured":"Peng, J., Li, J., Shang, X.: A learning-based method for drug-target interaction prediction based on feature representation learning and deep neural network. BMC Bioinform. 21(Suppl 13), 394 (2020)","key":"54_CR8","DOI":"10.1186\/s12859-020-03677-1"},{"issue":"12","key":"54_CR9","doi-asserted-by":"publisher","first-page":"246","DOI":"10.1093\/bioinformatics\/btq176","volume":"26","author":"Y Yamanishi","year":"2010","unstructured":"Yamanishi, Y., Kotera, M., Kanehisa, M., Goto, S.: Drug-target interaction prediction from chemical, genomic and pharmacological data in an integrated framework. Bioinformatics 26(12), 246\u2013254 (2010)","journal-title":"Bioinformatics"},{"doi-asserted-by":"crossref","unstructured":"Huang, Y., et al.: Predicting drug-target on heterogeneous network with co-rank. In: The 8th International Conference on Computer Engineering and Networks, pp. 571\u201381 (2020)","key":"54_CR10","DOI":"10.1007\/978-3-030-14680-1_63"},{"doi-asserted-by":"crossref","unstructured":"Cheng, F., et al.: Prediction of drug-target interactions and drug repositioning via network-based inference. PLoS Comput. Biol. 8(5), 1002503 (2012)","key":"54_CR11","DOI":"10.1371\/journal.pcbi.1002503"},{"doi-asserted-by":"crossref","unstructured":"Zzat, A.E., Zhao, P., Min, W., et al.: Drug-target interaction prediction with graph regularized matrix factorization. IEEE\/ACM Trans. Comput. Biol. Bioinform. 14(3), 1 (2016)","key":"54_CR12","DOI":"10.1109\/TCBB.2016.2530062"},{"doi-asserted-by":"crossref","unstructured":"Yamanishi, Y., Araki, M., Gutteridge, A., Honda, W., Kanehisa, M.: Prediction of drug\u2013target interaction networks from the integration of chemical and genomic spaces. Bioinformatics 24(13), i232\u2013i240 (2008)","key":"54_CR13","DOI":"10.1093\/bioinformatics\/btn162"},{"doi-asserted-by":"crossref","unstructured":"Mei, J.-P., Kwoh, C.-K., Yang, P., Li, X.-L., Zheng, J.: Drug\u2013target interaction prediction by learning from local information and neighbors, Bioinformatics 29(2), 238\u2013245 (2013)","key":"54_CR14","DOI":"10.1093\/bioinformatics\/bts670"},{"doi-asserted-by":"crossref","unstructured":"Ba-Alawi, W., et al.: DASPfind: new efficient method to predict drug-target interactions. Cheminform 8(1), 15 (2016)","key":"54_CR15","DOI":"10.1186\/s13321-016-0128-4"},{"doi-asserted-by":"crossref","unstructured":"Daminelli, S., et al.: Common neighbours and the local-community-paradigm for topological link prediction in bipartite networks. New J. Phys. 17(11), 113037 (2015)","key":"54_CR16","DOI":"10.1088\/1367-2630\/17\/11\/113037"},{"key":"54_CR17","doi-asserted-by":"publisher","first-page":"D354","DOI":"10.1093\/nar\/gkj102","volume":"34","author":"M Kanehisa","year":"2006","unstructured":"Kanehisa, M., et al.: From genomics to chemical genomics: new developments in KEGG. Nucleic Acids Res. 34, D354\u2013D357 (2006)","journal-title":"Nucleic Acids Res."},{"key":"54_CR18","doi-asserted-by":"publisher","first-page":"D919","DOI":"10.1093\/nar\/gkm862","volume":"36","author":"S Gunther","year":"2008","unstructured":"Gunther, S., et al.: Super target and matador: resources for exploring drug-target relationships. Nucleic Acids Res. 36, D919\u2013D922 (2008)","journal-title":"Nucleic Acids Res."},{"key":"54_CR19","doi-asserted-by":"publisher","first-page":"D901","DOI":"10.1093\/nar\/gkm958","volume":"36","author":"DS Wishart","year":"2008","unstructured":"Wishart, D.S., et al.: DrugBank: a knowledgebase for drugs, drug actions and drug targets. Nucleic Acids Res. 36, D901\u2013D906 (2008)","journal-title":"Nucleic Acids Res."},{"key":"54_CR20","doi-asserted-by":"publisher","first-page":"D431","DOI":"10.1093\/nar\/gkh081","volume":"32","author":"I Schomburg","year":"2004","unstructured":"Schomburg, I.: BRENDA, the enzyme database: updates and major new developments. Nucleic Acids Res. 32, D431\u2013D433 (2004)","journal-title":"Nucleic Acids Res."},{"issue":"D1","key":"54_CR21","doi-asserted-by":"publisher","first-page":"D1075","DOI":"10.1093\/nar\/gkv1075","volume":"44","author":"M Kuhn","year":"2016","unstructured":"Kuhn, M., Letunic, I., Jensen, L.J., et al.: The SIDER database of drugs and side effects. Nucleic Acids Res. 44(D1), D1075\u2013D1079 (2016)","journal-title":"Nucleic Acids Res."},{"doi-asserted-by":"crossref","unstructured":"Alanis-Lobato, G., Andrade-Navarro, M.A., Schaefer, M.H.: HIPPIE v2.0: enhancing meaningfulness and reliability of protein\u2013protein interaction networks. Nucleic Acids Res. 45(D1), D408\u2013D414 (2017)","key":"54_CR22","DOI":"10.1093\/nar\/gkw985"},{"issue":"5886","key":"54_CR23","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1126\/science.1158140","volume":"321","author":"M Campillos","year":"2008","unstructured":"Campillos, M., Kuhn, M., Gavin, A.C., et al.: Drug target identification using side-effect similarity. Science 321(5886), 263\u2013266 (2008)","journal-title":"Science"},{"doi-asserted-by":"crossref","unstructured":"Vilar, S., Hripcsak, G.: The role of drug profiles as similarity metrics: applications to repurposing, adverse effects detection and drug-drug interactions. Brief. Bioinform. 18(4), bbw048 (2016)","key":"54_CR24","DOI":"10.1093\/bib\/bbw048"},{"unstructured":"Deng, M., et al.: Prediction of protein function using protein-protein interaction data. In: Proceedings IEEE Computer Society Bioinformatics Conference EE (2002)","key":"54_CR25"},{"doi-asserted-by":"crossref","unstructured":"Shen, C., Luo, J., Lai, Z., et al.: Multiview joint learning-based method for identifying small-molecule-associated MiRNAs by integrating pharmacological, genomics, and network knowledge. J. Chem. Inf. Model. (2020)","key":"54_CR26","DOI":"10.1021\/acs.jcim.0c00244"},{"unstructured":"Lin, Z., Chen, M., Ma, Y.: The augmented Lagrange multiplier method for exact recovery of corrupted low-rank matrices. eprint arxiv:9 (2010)","key":"54_CR27"},{"doi-asserted-by":"crossref","unstructured":"Pech, R., Hao, D., Lee, Y.L., et al.: Link prediction via linear optimization. Phys. A: Stat. Mech. Appl. 528 (2019)","key":"54_CR28","DOI":"10.1016\/j.physa.2019.121319"},{"doi-asserted-by":"crossref","unstructured":"van Laarhoven, T., Marchiori, E.: Predicting drug-target interactions for new drug compounds using a weighted nearest neighbor profile. PLoS ONE 8(6), e66952 (2013)","key":"54_CR29","DOI":"10.1371\/journal.pone.0066952"},{"doi-asserted-by":"crossref","unstructured":"Zheng, X., Ding, H., Mamitsuka, H., Zhu, S.: Collaborative matrix factorization with multiple similarities for predicting drug-target interactions. In: KDD 2013: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1025\u20131033 (2013)","key":"54_CR30","DOI":"10.1145\/2487575.2487670"},{"doi-asserted-by":"crossref","unstructured":"Liu, Y., et al.: Neighborhood regularized logistic matrix factorization for drug-target interaction prediction. PLoS Comput. Biol. 12 e1004760 (2016)","key":"54_CR31","DOI":"10.1371\/journal.pcbi.1004760"},{"doi-asserted-by":"crossref","unstructured":"Ding, Y., Tang, J., Guo, F.: Identification of drug-target interactions via dual Laplacian regularized least squares with multiple Kernel fusion. Knowl.-Based Syst. 204, 106254 (2020)","key":"54_CR32","DOI":"10.1016\/j.knosys.2020.106254"}],"container-title":["Lecture Notes in Computer Science","Intelligent Computing Theories and Application"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-84532-2_54","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,8,8]],"date-time":"2021-08-08T23:07:33Z","timestamp":1628464053000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-84532-2_54"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030845315","9783030845322"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-84532-2_54","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"9 August 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shenzhen","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 August 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 August 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icic2021a","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ic-icc.cn\/2021\/index.htm","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}