{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:40:04Z","timestamp":1777704004336,"version":"3.51.4"},"reference-count":27,"publisher":"SAGE Publications","issue":"2","license":[{"start":{"date-parts":[[2018,6,18]],"date-time":"2018-06-18T00:00:00Z","timestamp":1529280000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[2018,8,26]]},"abstract":"<jats:p>Human Cancer Cell lines have gained a lot of attention since it helps in studying cancer biology and various treatment options. Recently various large-scale drug screening experiments were performed providing access to genomic and pharmacological data. This data helps in predicting drug responses which eventually contributes to the development of personalized cancer treatment. Heterogeneous nature of cancer raises the serious need for therapeutic agents with an essence of personalized treatment. Thus considering the assumption that similar drugs exhibit similar drug responses, we have developed kernelized similarity based regularization matrix factorization framework for predicting anti-cancer drug responses. Drug-Drug chemical structure similarity and Tissue-Tissue similarity (gene expression) are taken as key descriptors to formulate the objective function. The kernel function is used to map non-linear relationships between drugs and tissues. Our aim is to provide an efficient anti-cancer drug response prediction approach to establish the protocol for personalized treatment and new drugs designing. The proposed framework is validated using publicly available tumor datasets: GDSC and CCLE. Proposed KSRMF is further compared with three states of art algorithms using GDSC and CCLE drug screens. We have also predicted missing drug response values in the dataset using KSRMF. KSRMF outperforms other counterparts even though gene mutation data is not incorporated while designing the approach. An average mean square error of 3.24 and 0.504 is achieved using GDSC and CCLE drug screens respectively. The obtained results show that the proposed framework has quite potential to improve anti-cancer drug response prediction. Our analysis showed how data integration can help in achieving the goal of personalized cancer treatment.<\/jats:p>","DOI":"10.3233\/jifs-169713","type":"journal-article","created":{"date-parts":[[2018,6,19]],"date-time":"2018-06-19T15:05:15Z","timestamp":1529420715000},"page":"1779-1790","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":13,"title":["KSRMF: Kernelized similarity based regularized matrix factorization framework for predicting anti-cancer drug responses"],"prefix":"10.1177","volume":"35","author":[{"given":"Aman","family":"Sharma","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Thapar University, Patiala, Punjab, India"}]},{"given":"Rinkle","family":"Rani","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Thapar University, Patiala, Punjab, India"}]}],"member":"179","published-online":{"date-parts":[[2018,6,18]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1158\/1078-0432.CCR-13-2127"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1038\/nature11005"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1038\/nature11003"},{"key":"e_1_3_1_5_2","first-page":"1215","volume-title":"Proceedings of the 23rd ACMSIGKDD International Conference on Knowledge Discovery and Data Mining","author":"Yamada M.","year":"2017","unstructured":"YamadaM., LianW., GoyalA., ChenJ., WimalawarneK., KhanS.A., KaskiS., MamitsukaH. and ChangY., Convex factorization machine for toxicogenomics prediction. In Proceedings of the 23rd ACMSIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, 2017, pp. 1215\u20131224."},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1145\/1864708.1864736"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12885-017-3500-5"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2015.2412522"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1126\/science.1158140"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1021\/ci500152b"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1004498"},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0061318"},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.1186\/gb-2013-14-10-r110"},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1038\/srep23857"},{"key":"e_1_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.1038\/ng.2764"},{"key":"e_1_3_1_16_2","first-page":"27","volume-title":"Pacific Symposium on Biocomputing","author":"Neto E.C.","year":"2014","unstructured":"NetoE.C., JangI.S., FriendS.H., and MargolinA.A., The stream algorithm: Computationally efficient ridge regression via Bayesian model averaging, and applications to pharmacogenomic prediction ofcancer cell line sensitivity. In Pacific Symposium on Biocomputing, NIH Public Access, 2014, p. 27."},{"key":"e_1_3_1_17_2","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btt331"},{"key":"e_1_3_1_18_2","first-page":"17355","volume-title":"Proceedings of the National Academy of Sciences","volume":"103","author":"Rhee S.Y.","year":"2006","unstructured":"RheeS.Y., TaylorJ., WadheraG., Ben-HurA., BrutlagD.L. and ShaferR.W., Genotypic predictors of human immunodeficiency virus type 1drug resistance, Proceedings of the National Academy of Sciences103 (46) (2006), 17355\u201317360."},{"key":"e_1_3_1_19_2","first-page":"339","volume-title":"Proceedings of the twenty-fifth conference on uncertainty in artificial intelligence","author":"Liu J.","year":"2009","unstructured":"LiuJ., JiS., YeJ., June. Multi-task feature learning via efficient l 2, 1-norm minimization. In Proceedings of the twenty-fifth conference on uncertainty in artificial intelligence, AUAI Press, 2009, 339\u2013348."},{"key":"e_1_3_1_20_2","first-page":"2187","article-title":"Trace lasso: A trace norm regularization for correlated designs","author":"Grave E.","year":"2011","unstructured":"GraveE., ObozinskiG.R., and BachF.R., Trace lasso: A trace norm regularization for correlated designs, In Advances in Neural Information Processing Systems, 2011, 2187\u20132195.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_1_21_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10589-009-9283-0"},{"key":"e_1_3_1_22_2","first-page":"2307","article-title":"Expression of p16 and retinoblastoma determines response toCDK 4\/6 inhibition in ovarian cancer","author":"Konecny G.E.","year":"2011","unstructured":"KonecnyG.E., WinterhoffB., KolarovaT., QiJ., ManivongK., DeringJ., GuorongE., ChalukyaM., WangH.J., AndersonL. and KalliK.R., Expression of p16 and retinoblastoma determines response toCDK 4\/6 inhibition in ovarian cancer, Clinical Cancer Research, 2011, pp.clincanres-2307.","journal-title":"Clinical Cancer Research"},{"key":"e_1_3_1_23_2","doi-asserted-by":"crossref","unstructured":"BuhlI.K. ChristensenI.J. Santoni-RugiuE. RavnJ. HansenA. JensenT. AskaaJ. JensenP.B. KnudsenS. and SoerensenJ.B. Multigene expression profile for predicting efficacy of cisplatinand vinorelbine in non-small cell lung cancer 2016.","DOI":"10.1093\/annonc\/mdw382.01"},{"key":"e_1_3_1_24_2","first-page":"4","article-title":"The c-Abl inhibitor, nilotinib, protects dopaminergic neurons in a preclinical animal model of Parkinson\u2019s disease","author":"Karuppagounder S.S.","year":"2014","unstructured":"KaruppagounderS.S., BrahmachariS., LeeY., DawsonV.L., DawsonT.M. and KoH.S., The c-Abl inhibitor, nilotinib, protects dopaminergic neurons in a preclinical animal model of Parkinson\u2019s disease, Scientific Reports (2014), 4.","journal-title":"Scientific Reports"},{"key":"e_1_3_1_25_2","doi-asserted-by":"crossref","unstructured":"ZhengX. DingH. MamitsukaH. and ZhuS. Collaborative matrix factorization with multiple similarities for predicting drug-target interactions. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining 2013 August pp. ACM. 1025\u20131033.","DOI":"10.1145\/2487575.2487670"},{"key":"e_1_3_1_26_2","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btv529"},{"key":"e_1_3_1_27_2","first-page":"1","article-title":"DFuzzy: A deep learning-based fuzzy clustering model for large graphs","author":"Bhatia V.","year":"2018","unstructured":"BhatiaV.and RaniR., DFuzzy: A deep learning-based fuzzy clustering model for large graphs, Knowledge and Information Systems2018. doi:10.1007\/s10115-018-1156-3, 1\u201323.","journal-title":"Knowledge and Information Systems"},{"key":"e_1_3_1_28_2","doi-asserted-by":"publisher","DOI":"10.1166\/jmihi.2017.2266"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-169713","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.3233\/JIFS-169713","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-169713","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:40:11Z","timestamp":1777455611000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.3233\/JIFS-169713"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,6,18]]},"references-count":27,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2018,8,26]]}},"alternative-id":["10.3233\/JIFS-169713"],"URL":"https:\/\/doi.org\/10.3233\/jifs-169713","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,6,18]]}}}