{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T12:27:41Z","timestamp":1781094461002,"version":"3.54.1"},"reference-count":86,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,2,3]],"date-time":"2023-02-03T00:00:00Z","timestamp":1675382400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,2,3]],"date-time":"2023-02-03T00:00:00Z","timestamp":1675382400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61976239"],"award-info":[{"award-number":["61976239"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003453","name":"Natural Science Foundation of Guangdong Province","doi-asserted-by":"publisher","award":["2020A1515010783"],"award-info":[{"award-number":["2020A1515010783"]}],"id":[{"id":"10.13039\/501100003453","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>The experimental verification of a drug discovery process is expensive and time-consuming. Therefore, efficiently and effectively identifying drug\u2013target interactions (DTIs) has been the focus of research. At present, many machine learning algorithms are used for predicting DTIs. The key idea is to train the classifier using an existing DTI to predict a new or unknown DTI. However, there are various challenges, such as class imbalance and the parameter optimization of many classifiers, that need to be solved before an optimal DTI model is developed.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>In this study, we propose a framework called SSELM-neg for DTI prediction, in which we use a screening approach to choose high-quality negative samples and a spherical search approach to optimize the parameters of the extreme learning machine.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The results demonstrated that the proposed technique outperformed other state-of-the-art methods in 10-fold cross-validation experiments in terms of the area under the receiver operating characteristic curve (0.986, 0.993, 0.988, and 0.969) and AUPR (0.982, 0.991, 0.982, and 0.946) for the enzyme dataset, G-protein coupled receptor dataset, ion channel dataset, and nuclear receptor dataset, respectively.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The screening approach produced high-quality negative samples with the same number of positive samples, which solved the class imbalance problem. We optimized an extreme learning machine using a spherical search approach to identify DTIs. Therefore, our models performed better than other state-of-the-art methods.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12859-023-05153-y","type":"journal-article","created":{"date-parts":[[2023,2,3]],"date-time":"2023-02-03T03:04:33Z","timestamp":1675393473000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["SSELM-neg: spherical search-based extreme learning machine for drug\u2013target interaction prediction"],"prefix":"10.1186","volume":"24","author":[{"given":"Lingzhi","family":"Hu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chengzhou","family":"Fu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhonglu","family":"Ren","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongming","family":"Cai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jin","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Siwen","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenhua","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Deyu","family":"Tang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,2,3]]},"reference":[{"issue":"4","key":"5153_CR1","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1093\/bib\/bbr013","volume":"12","author":"JT Dudley","year":"2011","unstructured":"Dudley JT, Deshpande T, Butte AJ. Exploiting drug\u2013disease relationships for computational drug repositioning. Brief Bioinform. 2011;12(4):303\u201311.","journal-title":"Brief Bioinform"},{"issue":"4","key":"5153_CR2","doi-asserted-by":"publisher","first-page":"327","DOI":"10.1093\/bib\/bbr028","volume":"12","author":"SJ Swamidass","year":"2011","unstructured":"Swamidass SJ. Mining small-molecule screens to repurpose drugs. Brief Bioinform. 2011;12(4):327\u201335.","journal-title":"Brief Bioinform"},{"issue":"4","key":"5153_CR3","doi-asserted-by":"publisher","first-page":"696","DOI":"10.1093\/bib\/bbv066","volume":"17","author":"X Chen","year":"2016","unstructured":"Chen X, Yan CC, Zhang X, Zhang X, Dai F, Yin J, Zhang Y. Drug\u2013target interaction prediction: databases, web servers and computational models. Brief Bioinform. 2016;17(4):696\u2013712.","journal-title":"Brief Bioinform"},{"issue":"1","key":"5153_CR4","first-page":"47","volume":"21","author":"X Chen","year":"2020","unstructured":"Chen X, Guan N-N, Sun Y-Z, Li J-Q, Qu J. Microrna-small molecule association identification: from experimental results to computational models. Brief Bioinform. 2020;21(1):47\u201361.","journal-title":"Brief Bioinform"},{"issue":"7270","key":"5153_CR5","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1038\/462167a","volume":"462","author":"AL Hopkins","year":"2009","unstructured":"Hopkins AL. Predicting promiscuity. Nature. 2009;462(7270):167\u20138.","journal-title":"Nature"},{"issue":"7403","key":"5153_CR6","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1038\/nature11159","volume":"486","author":"E Lounkine","year":"2012","unstructured":"Lounkine E, Keiser MJ, Whitebread S, Mikhailov D, Hamon J, Jenkins JL, Lavan P, Weber E, Doak AK, C\u00f4t\u00e9 S, et al. Large-scale prediction and testing of drug activity on side-effect targets. Nature. 2012;486(7403):361\u20137.","journal-title":"Nature"},{"issue":"1","key":"5153_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1471-2105-12-169","volume":"12","author":"E Pauwels","year":"2011","unstructured":"Pauwels E, Stoven V, Yamanishi Y. Predicting drug side-effect profiles: a chemical fragment-based approach. BMC Bioinform. 2011;12(1):1\u201313.","journal-title":"BMC Bioinform"},{"issue":"21","key":"5153_CR8","doi-asserted-by":"publisher","first-page":"1421","DOI":"10.1016\/S1359-6446(05)03632-9","volume":"10","author":"S Whitebread","year":"2005","unstructured":"Whitebread S, Hamon J, Bojanic D, Urban L. Keynote review: in vitro safety pharmacology profiling: an essential tool for successful drug development. Drug Discov Today. 2005;10(21):1421\u201333.","journal-title":"Drug Discov Today"},{"issue":"5","key":"5153_CR9","doi-asserted-by":"publisher","first-page":"383","DOI":"10.1016\/S1074-5521(03)00095-4","volume":"10","author":"SJ Haggarty","year":"2003","unstructured":"Haggarty SJ, Koeller KM, Wong JC, Butcher RA, Schreiber SL. Multidimensional chemical genetic analysis of diversity-oriented synthesis-derived deacetylase inhibitors using cell-based assays. Chem Biol. 2003;10(5):383\u201396.","journal-title":"Chem Biol"},{"issue":"21","key":"5153_CR10","doi-asserted-by":"publisher","first-page":"1101","DOI":"10.1016\/S1359-6446(01)01990-0","volume":"6","author":"CJ Manly","year":"2001","unstructured":"Manly CJ, Louise-May S, Hammer JD. The impact of informatics and computational chemistry on synthesis and screening. Drug Discov Today. 2001;6(21):1101\u201310.","journal-title":"Drug Discov Today"},{"issue":"1","key":"5153_CR11","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1093\/bib\/bbv020","volume":"17","author":"J Li","year":"2016","unstructured":"Li J, Zheng S, Chen B, Butte AJ, Swamidass SJ, Lu Z. A survey of current trends in computational drug repositioning. Brief Bioinform. 2016;17(1):2\u201312.","journal-title":"Brief Bioinform"},{"issue":"1","key":"5153_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12859-021-04327-w","volume":"22","author":"Y Yue","year":"2021","unstructured":"Yue Y, He S. Dti-hene: a novel method for drug\u2013target interaction prediction based on heterogeneous network embedding. BMC Bioinform. 2021;22(1):1\u201320.","journal-title":"BMC Bioinform"},{"issue":"2","key":"5153_CR13","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1038\/nbt1284","volume":"25","author":"MJ Keiser","year":"2007","unstructured":"Keiser MJ, Roth BL, Armbruster BN, Ernsberger P, Irwin JJ, Shoichet BK. Relating protein pharmacology by ligand chemistry. Nat Biotechnol. 2007;25(2):197\u2013206.","journal-title":"Nat Biotechnol"},{"issue":"4","key":"5153_CR14","doi-asserted-by":"publisher","first-page":"1337","DOI":"10.1093\/bib\/bby002","volume":"20","author":"A Ezzat","year":"2019","unstructured":"Ezzat A, Wu M, Li X-L, Kwoh C-K. Computational prediction of drug\u2013target interactions using chemogenomic approaches: an empirical survey. Brief Bioinform. 2019;20(4):1337\u201357.","journal-title":"Brief Bioinform"},{"issue":"suppl 2","key":"5153_CR15","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1093\/nar\/gkl114","volume":"34","author":"H Li","year":"2006","unstructured":"Li H, Gao Z, Kang L, Zhang H, Yang K, Yu K, Luo X, Zhu W, Chen K, Shen J, et al. Tarfisdock: a web server for identifying drug targets with docking approach. Nucleic Acids Res. 2006;34(suppl 2):219\u201324.","journal-title":"Nucleic Acids Res"},{"issue":"1","key":"5153_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.2174\/157341208783497597","volume":"4","author":"G Pujadas","year":"2008","unstructured":"Pujadas G, Vaque M, Ardevol A, Blade C, Salvado M, Blay M, Fernandez-Larrea J, Arola L. Protein-ligand docking: a review of recent advances and future perspectives. Curr Pharm Anal. 2008;4(1):1\u201319.","journal-title":"Curr Pharm Anal"},{"issue":"1","key":"5153_CR17","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1038\/nbt1273","volume":"25","author":"AC Cheng","year":"2007","unstructured":"Cheng AC, Coleman RG, Smyth KT, Cao Q, Soulard P, Caffrey DR, Salzberg AC, Huang ES. Structure-based maximal affinity model predicts small-molecule druggability. Nat Biotechnol. 2007;25(1):71\u20135.","journal-title":"Nat Biotechnol"},{"issue":"5009","key":"5153_CR18","doi-asserted-by":"publisher","first-page":"1189","DOI":"10.1126\/science.252.5009.1189.a","volume":"252","author":"JB Hendrickson","year":"1991","unstructured":"Hendrickson JB. Concepts and applications of molecular similarity. Science. 1991;252(5009):1189\u201390.","journal-title":"Science"},{"issue":"19","key":"5153_CR19","doi-asserted-by":"publisher","first-page":"2149","DOI":"10.1093\/bioinformatics\/btn409","volume":"24","author":"L Jacob","year":"2008","unstructured":"Jacob L, Vert J-P. Protein\u2013ligand interaction prediction: an improved chemogenomics approach. Bioinformatics. 2008;24(19):2149\u201356.","journal-title":"Bioinformatics"},{"key":"5153_CR20","volume":"18","author":"T Ban","year":"2019","unstructured":"Ban T, Ohue M, Akiyama Y. Nrlmf\u03b2: Beta-distribution-rescored neighborhood regularized logistic matrix factorization for improving the performance of drug\u2013target interaction prediction. Biochem Biophys Rep. 2019;18: 100615.","journal-title":"Biochem Biophys Rep"},{"key":"5153_CR21","first-page":"2021","volume":"66","author":"A Wang","year":"2021","unstructured":"Wang A, Wang M. Drug\u2013target interaction prediction via dual Laplacian graph regularized logistic matrix factorization. BioMed Res Int. 2021;66:2021.","journal-title":"BioMed Res Int"},{"issue":"5","key":"5153_CR22","doi-asserted-by":"publisher","first-page":"1712","DOI":"10.1109\/TCBB.2017.2706267","volume":"16","author":"L Li","year":"2017","unstructured":"Li L, Cai M. Drug target prediction by multi-view low rank embedding. IEEE\/ACM Trans Comput Biol Bioinform. 2017;16(5):1712\u201321.","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"issue":"13","key":"5153_CR23","doi-asserted-by":"publisher","first-page":"232","DOI":"10.1093\/bioinformatics\/btn162","volume":"24","author":"Y Yamanishi","year":"2008","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. 2008;24(13):232\u201340.","journal-title":"Bioinformatics"},{"issue":"2","key":"5153_CR24","doi-asserted-by":"publisher","first-page":"238","DOI":"10.1093\/bioinformatics\/bts670","volume":"29","author":"J-P Mei","year":"2013","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. 2013;29(2):238\u201345.","journal-title":"Bioinformatics"},{"issue":"13","key":"5153_CR25","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1093\/bioinformatics\/btm204","volume":"23","author":"K Bleakley","year":"2007","unstructured":"Bleakley K, Biau G, Vert J-P. Supervised reconstruction of biological networks with local models. Bioinformatics. 2007;23(13):57\u201365.","journal-title":"Bioinformatics"},{"key":"5153_CR26","doi-asserted-by":"publisher","first-page":"284","DOI":"10.1016\/j.neucom.2017.04.055","volume":"260","author":"K Buza","year":"2017","unstructured":"Buza K, Pe\u0161ka L. Drug\u2013target interaction prediction with bipartite local models and hubness-aware regression. Neurocomputing. 2017;260:284\u201393.","journal-title":"Neurocomputing"},{"key":"5153_CR27","doi-asserted-by":"crossref","unstructured":"Buza K. Drug\u2013target interaction prediction with hubness-aware machine learning. In: 2016 IEEE 11th International Symposium on Applied Computational Intelligence and Informatics (SACI). IEEE; 2016. p. 437\u201340.","DOI":"10.1109\/SACI.2016.7507416"},{"key":"5153_CR28","doi-asserted-by":"publisher","first-page":"250","DOI":"10.1016\/j.knosys.2015.06.010","volume":"86","author":"K Buza","year":"2015","unstructured":"Buza K, Nanopoulos A, Nagy G. Nearest neighbor regression in the presence of bad hubs. Knowl Based Syst. 2015;86:250\u201360.","journal-title":"Knowl Based Syst"},{"issue":"15","key":"5153_CR29","doi-asserted-by":"publisher","first-page":"2337","DOI":"10.1093\/bioinformatics\/btx160","volume":"33","author":"N Zong","year":"2017","unstructured":"Zong N, Kim H, Ngo V, Harismendy O. Deep mining heterogeneous networks of biomedical linked data to predict novel drug\u2013target associations. Bioinformatics. 2017;33(15):2337\u201344.","journal-title":"Bioinformatics"},{"issue":"2","key":"5153_CR30","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1093\/bib\/bbu010","volume":"16","author":"T Pahikkala","year":"2015","unstructured":"Pahikkala T, Airola A, Pietil\u00e4 S, Shakyawar S, Szwajda A, Tang J, Aittokallio T. Toward more realistic drug\u2013target interaction predictions. Brief Bioinform. 2015;16(2):325\u201337.","journal-title":"Brief Bioinform"},{"issue":"1","key":"5153_CR31","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1002\/minf.201501008","volume":"35","author":"E Gawehn","year":"2016","unstructured":"Gawehn E, Hiss JA, Schneider G. Deep learning in drug discovery. Mol Inform. 2016;35(1):3\u201314.","journal-title":"Mol Inform"},{"issue":"11","key":"5153_CR32","doi-asserted-by":"publisher","first-page":"2594","DOI":"10.1007\/s11095-016-2029-7","volume":"33","author":"S Ekins","year":"2016","unstructured":"Ekins S. The next era: deep learning in pharmaceutical research. Pharm Res. 2016;33(11):2594\u2013603.","journal-title":"Pharm Res"},{"issue":"5786","key":"5153_CR33","doi-asserted-by":"publisher","first-page":"504","DOI":"10.1126\/science.1127647","volume":"313","author":"GE Hinton","year":"2006","unstructured":"Hinton GE, Salakhutdinov RR. Reducing the dimensionality of data with neural networks. Science. 2006;313(5786):504\u20137.","journal-title":"Science"},{"issue":"4","key":"5153_CR34","doi-asserted-by":"publisher","first-page":"1401","DOI":"10.1021\/acs.jproteome.6b00618","volume":"16","author":"M Wen","year":"2017","unstructured":"Wen M, Zhang Z, Niu S, Sha H, Yang R, Yun Y, Lu H. Deep-learning-based drug\u2013target interaction prediction. J Proteome Res. 2017;16(4):1401\u20139.","journal-title":"J Proteome Res"},{"issue":"6","key":"5153_CR35","doi-asserted-by":"publisher","first-page":"1007129","DOI":"10.1371\/journal.pcbi.1007129","volume":"15","author":"I Lee","year":"2019","unstructured":"Lee I, Keum J, Nam H. Deepconv-dti: prediction of drug\u2013target interactions via deep learning with convolution on protein sequences. PLoS Comput Biol. 2019;15(6):1007129.","journal-title":"PLoS Comput Biol"},{"key":"5153_CR36","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1016\/j.compbiolchem.2019.03.016","volume":"80","author":"J You","year":"2019","unstructured":"You J, McLeod RD, Hu P. Predicting drug\u2013target interaction network using deep learning model. Comput Biol Chem. 2019;80:90\u2013101.","journal-title":"Comput Biol Chem"},{"issue":"5","key":"5153_CR37","doi-asserted-by":"publisher","first-page":"1878","DOI":"10.1093\/bib\/bby061","volume":"20","author":"AS Rifaioglu","year":"2019","unstructured":"Rifaioglu AS, Atas H, Martin MJ, Cetin-Atalay R, Atalay V, Do\u011fan T. Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases. Brief Bioinform. 2019;20(5):1878\u2013912.","journal-title":"Brief Bioinform"},{"issue":"5","key":"5153_CR38","doi-asserted-by":"publisher","first-page":"1402","DOI":"10.1021\/ci050006d","volume":"45","author":"JR Bock","year":"2005","unstructured":"Bock JR, Gough DA. Virtual screen for ligands of orphan g protein-coupled receptors. J Chem Inf Model. 2005;45(5):1402\u201314.","journal-title":"J Chem Inf Model"},{"issue":"15","key":"5153_CR39","doi-asserted-by":"publisher","first-page":"2004","DOI":"10.1093\/bioinformatics\/btm266","volume":"23","author":"N Nagamine","year":"2007","unstructured":"Nagamine N, Sakakibara Y. Statistical prediction of protein\u2013chemical interactions based on chemical structure and mass spectrometry data. Bioinformatics. 2007;23(15):2004\u201312.","journal-title":"Bioinformatics"},{"issue":"8","key":"5153_CR40","doi-asserted-by":"publisher","first-page":"1781","DOI":"10.3390\/ijms18081781","volume":"18","author":"C Shen","year":"2017","unstructured":"Shen C, Ding Y, Tang J, Xu X, Guo F. An ameliorated prediction of drug\u2013target interactions based on multi-scale discrete wavelet transform and network features. Int J Mol Sci. 2017;18(8):1781.","journal-title":"Int J Mol Sci"},{"key":"5153_CR41","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1016\/j.vascn.2015.11.002","volume":"78","author":"Z Mousavian","year":"2016","unstructured":"Mousavian Z, Khakabimamaghani S, Kavousi K, Masoudi-Nejad A. Drug\u2013target interaction prediction from pssm based evolutionary information. J Pharmacol Toxicol Methods. 2016;78:42\u201351.","journal-title":"J Pharmacol Toxicol Methods"},{"issue":"12","key":"5153_CR42","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1093\/bioinformatics\/btv256","volume":"31","author":"H Liu","year":"2015","unstructured":"Liu H, Sun J, Guan J, Zheng J, Zhou S. Improving compound\u2013protein interaction prediction by building up highly credible negative samples. Bioinformatics. 2015;31(12):221\u20139.","journal-title":"Bioinformatics"},{"key":"5153_CR43","unstructured":"Xia Z, Zhou X, Sun Y, Wu L. Semi-supervised drug\u2013protein interaction prediction from heterogeneous spaces. In: The third international symposium on optimization and systems biology, vol 11; 2009;. p. 123\u201331."},{"issue":"3","key":"5153_CR44","doi-asserted-by":"publisher","first-page":"646","DOI":"10.1109\/TCBB.2016.2530062","volume":"14","author":"A Ezzat","year":"2016","unstructured":"Ezzat A, Zhao P, Wu M, Li X-L, Kwoh C-K. Drug\u2013target interaction prediction with graph regularized matrix factorization. IEEE\/ACM Trans Comput Biol Bioinform. 2016;14(3):646\u201356.","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"issue":"18","key":"5153_CR45","doi-asserted-by":"publisher","first-page":"2304","DOI":"10.1093\/bioinformatics\/bts360","volume":"28","author":"M G\u00f6nen","year":"2012","unstructured":"G\u00f6nen M. Predicting drug\u2013target interactions from chemical and genomic kernels using Bayesian matrix factorization. Bioinformatics. 2012;28(18):2304\u201310.","journal-title":"Bioinformatics"},{"issue":"1","key":"5153_CR46","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41467-017-00680-8","volume":"8","author":"Y Luo","year":"2017","unstructured":"Luo Y, Zhao X, Zhou J, Yang J, Zhang Y, Kuang W, Peng J, Chen L, Zeng J. A network integration approach for drug\u2013target interaction prediction and computational drug repositioning from heterogeneous information. Nat Commun. 2017;8(1):1\u201313.","journal-title":"Nat Commun"},{"issue":"7","key":"5153_CR47","doi-asserted-by":"publisher","first-page":"1970","DOI":"10.1039\/c2mb00002d","volume":"8","author":"X Chen","year":"2012","unstructured":"Chen X, Liu M-X, Yan G-Y. Drug\u2013target interaction prediction by random walk on the heterogeneous network. Mol BioSyst. 2012;8(7):1970\u20138.","journal-title":"Mol BioSyst"},{"issue":"1","key":"5153_CR48","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13321-015-0089-z","volume":"7","author":"A Seal","year":"2015","unstructured":"Seal A, Ahn Y-Y, Wild DJ. Optimizing drug\u2013target interaction prediction based on random walk on heterogeneous networks. J Cheminform. 2015;7(1):1\u201312.","journal-title":"J Cheminform"},{"issue":"16","key":"5153_CR49","doi-asserted-by":"publisher","first-page":"2004","DOI":"10.1093\/bioinformatics\/btt307","volume":"29","author":"S Alaimo","year":"2013","unstructured":"Alaimo S, Pulvirenti A, Giugno R, Ferro A. Drug\u2013target interaction prediction through domain-tuned network-based inference. Bioinformatics. 2013;29(16):2004\u20138.","journal-title":"Bioinformatics"},{"key":"5153_CR50","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1016\/j.neucom.2018.07.060","volume":"317","author":"Y Wu","year":"2018","unstructured":"Wu Y, Zhang Y, Liu X, Cai Z, Cai Y. A multiobjective optimization-based sparse extreme learning machine algorithm. Neurocomputing. 2018;317:88\u2013100.","journal-title":"Neurocomputing"},{"issue":"5","key":"5153_CR51","doi-asserted-by":"publisher","first-page":"734","DOI":"10.1093\/bib\/bbt056","volume":"15","author":"H Ding","year":"2014","unstructured":"Ding H, Takigawa I, Mamitsuka H, Zhu S. Similarity-based machine learning methods for predicting drug\u2013target interactions: a brief review. Brief Bioinform. 2014;15(5):734\u201347.","journal-title":"Brief Bioinform"},{"issue":"1","key":"5153_CR52","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1093\/bib\/bbz157","volume":"22","author":"M Bagherian","year":"2021","unstructured":"Bagherian M, Sabeti E, Wang K, Sartor MA, Nikolovska-Coleska Z, Najarian K. Machine learning approaches and databases for prediction of drug\u2013target interaction: a survey paper. Brief Bioinform. 2021;22(1):247\u201369.","journal-title":"Brief Bioinform"},{"key":"5153_CR53","first-page":"66","volume":"6","author":"J You","year":"2019","unstructured":"You J, Robert D, Pingzhao M. Predicting drug\u2013target interaction network using deep learning model. Comput Biol Chem. 2019;6:66.","journal-title":"Comput Biol Chem"},{"key":"5153_CR54","first-page":"66","volume":"6","author":"K Bleakley","year":"2009","unstructured":"Bleakley K, Yamanishi Y. Supervised prediction of drug\u2013target interactions using bipartite local models. Bioinformatics. 2009;6:66.","journal-title":"Bioinformatics"},{"issue":"2","key":"5153_CR55","first-page":"135","volume":"12","author":"A Ghanbari Sorkhi","year":"2021","unstructured":"Ghanbari Sorkhi A, Iranpour Mobarakeh M, Hashemi SMR, Faridpour M. Predicting drug\u2013target interaction based on bilateral local models using a decision tree-based hybrid support vector machine. Int J Nonlinear Anal Appl. 2021;12(2):135\u201344.","journal-title":"Int J Nonlinear Anal Appl"},{"key":"5153_CR56","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1016\/j.neucom.2016.03.080","volume":"206","author":"W Lan","year":"2016","unstructured":"Lan W, Wang J, Li M, Liu J, Li Y, Wu F-X, Pan Y. Predicting drug\u2013target interaction using positive-unlabeled learning. Neurocomputing. 2016;206:50\u20137.","journal-title":"Neurocomputing"},{"key":"5153_CR57","first-page":"66","volume":"6","author":"J Jiang","year":"2017","unstructured":"Jiang J, Wang N, Chen P, Zhang J, Wang B. Drugecs: an ensemble system with feature subspaces for accurate drug\u2013target interaction prediction. BioMed Res Int. 2017;6:66.","journal-title":"BioMed Res Int"},{"key":"5153_CR58","doi-asserted-by":"crossref","unstructured":"Manoochehri HE, Nourani M. Predicting drug\u2013target interaction using deep matrix factorization. In: 2018 IEEE biomedical circuits and systems conference (BioCAS). IEEE; 2018. p. 1\u20134.","DOI":"10.1109\/BIOCAS.2018.8584817"},{"issue":"10","key":"5153_CR59","doi-asserted-by":"publisher","first-page":"669","DOI":"10.1002\/minf.201400009","volume":"33","author":"D-S Cao","year":"2014","unstructured":"Cao D-S, Zhang L-X, Tan G-S, Xiang Z, Zeng W-B, Xu Q-S, Chen AF. Computational prediction of drug target interactions using chemical, biological, and network features. Mol Inform. 2014;33(10):669\u201381.","journal-title":"Mol Inform"},{"issue":"6","key":"5153_CR60","doi-asserted-by":"publisher","first-page":"1839","DOI":"10.1016\/j.ygeno.2018.12.007","volume":"111","author":"H Shi","year":"2019","unstructured":"Shi H, Liu S, Chen J, Li X, Ma Q, Yu B. Predicting drug\u2013target interactions using lasso with random forest based on evolutionary information and chemical structure. Genomics. 2019;111(6):1839\u201352.","journal-title":"Genomics"},{"issue":"1\u20133","key":"5153_CR61","doi-asserted-by":"publisher","first-page":"262","DOI":"10.1016\/j.neucom.2008.01.016","volume":"72","author":"G Wang","year":"2008","unstructured":"Wang G, Zhao Y, Wang D. A protein secondary structure prediction framework based on the extreme learning machine. Neurocomputing. 2008;72(1\u20133):262\u20138.","journal-title":"Neurocomputing"},{"issue":"10\u201311","key":"5153_CR62","doi-asserted-by":"publisher","first-page":"2588","DOI":"10.1016\/j.patcog.2011.03.013","volume":"44","author":"AA Mohammed","year":"2011","unstructured":"Mohammed AA, Minhas R, Wu QJ, Sid-Ahmed MA. Human face recognition based on multidimensional pca and extreme learning machine. Pattern Recognit. 2011;44(10\u201311):2588\u201397.","journal-title":"Pattern Recognit"},{"key":"5153_CR63","doi-asserted-by":"crossref","unstructured":"Han K, Yu D, Tashev I. Speech emotion recognition using deep neural network and extreme learning machine. In: Interspeech 2014;2014.","DOI":"10.21437\/Interspeech.2014-57"},{"key":"5153_CR64","first-page":"1","volume":"66","author":"X Bi","year":"2018","unstructured":"Bi X, Ma H, Li J, Ma Y, Chen D. A positive and unlabeled learning framework based on extreme learning machine for drug\u2013drug interactions discovery. J Ambient Intell Human Comput. 2018;66:1\u201312.","journal-title":"J Ambient Intell Human Comput"},{"issue":"1","key":"5153_CR65","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13040-021-00242-1","volume":"14","author":"J-Y An","year":"2021","unstructured":"An J-Y, Meng F-R, Yan Z-J. An efficient computational method for predicting drug\u2013target interactions using weighted extreme learning machine and speed up robot features. BioData Min. 2021;14(1):1\u201317.","journal-title":"BioData Min"},{"key":"5153_CR66","doi-asserted-by":"crossref","unstructured":"Huang G-B, Zhu Q-Y, Siew C-K. Extreme learning machine: a new learning scheme of feedforward neural networks. In: 2004 IEEE international joint conference on neural networks (IEEE Cat. No. 04CH37541), vol 2. IEEE; 2004. p. 985\u201390.","DOI":"10.1109\/IJCNN.2004.1380068"},{"key":"5153_CR67","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2019.105734","volume":"85","author":"A Kumar","year":"2019","unstructured":"Kumar A, Misra RK, Singh D, Mishra S, Das S. The spherical search algorithm for bound-constrained global optimization problems. Appl Soft Comput. 2019;85: 105734.","journal-title":"Appl Soft Comput"},{"key":"5153_CR68","doi-asserted-by":"crossref","unstructured":"Tanabe R, Fukunaga AS. Improving the search performance of shade using linear population size reduction. In: 2014 IEEE congress on evolutionary computation (CEC). IEEE; 2014. p. 1658\u201365.","DOI":"10.1109\/CEC.2014.6900380"},{"issue":"suppl 1","key":"5153_CR69","doi-asserted-by":"publisher","first-page":"901","DOI":"10.1093\/nar\/gkm958","volume":"36","author":"DS Wishart","year":"2008","unstructured":"Wishart DS, Knox C, Guo AC, Cheng D, Shrivastava S, Tzur D, Gautam B, Hassanali M. Drugbank: a knowledgebase for drugs, drug actions and drug targets. Nucleic Acids Res. 2008;36(suppl 1):901\u20136.","journal-title":"Nucleic Acids Res"},{"issue":"suppl 1","key":"5153_CR70","doi-asserted-by":"publisher","first-page":"919","DOI":"10.1093\/nar\/gkm862","volume":"36","author":"S G\u00fcnther","year":"2007","unstructured":"G\u00fcnther S, Kuhn M, Dunkel M, Campillos M, Senger C, Petsalaki E, Ahmed J, Urdiales EG, Gewiess A, Jensen LJ, et al. Supertarget and matador: resources for exploring drug\u2013target relationships. Nucleic Acids Res. 2007;36(suppl 1):919\u201322.","journal-title":"Nucleic Acids Res"},{"issue":"suppl 1","key":"5153_CR71","doi-asserted-by":"publisher","first-page":"354","DOI":"10.1093\/nar\/gkj102","volume":"34","author":"M Kanehisa","year":"2006","unstructured":"Kanehisa M, Goto S, Hattori M, Aoki-Kinoshita KF, Itoh M, Kawashima S, Katayama T, Araki M, Hirakawa M. From genomics to chemical genomics: new developments in Kegg. Nucleic Acids Res. 2006;34(suppl 1):354\u20137.","journal-title":"Nucleic Acids Res"},{"issue":"suppl 1","key":"5153_CR72","doi-asserted-by":"publisher","first-page":"431","DOI":"10.1093\/nar\/gkh081","volume":"32","author":"I Schomburg","year":"2004","unstructured":"Schomburg I, Chang A, Ebeling C, Gremse M, Heldt C, Huhn G, Schomburg D. Brenda, the enzyme database: updates and major new developments. Nucleic Acids Res. 2004;32(suppl 1):431\u20133.","journal-title":"Nucleic Acids Res"},{"issue":"39","key":"5153_CR73","doi-asserted-by":"publisher","first-page":"11853","DOI":"10.1021\/ja036030u","volume":"125","author":"M Hattori","year":"2003","unstructured":"Hattori M, Okuno Y, Goto S, Kanehisa M. Development of a chemical structure comparison method for integrated analysis of chemical and genomic information in the metabolic pathways. J Am Chem Soc. 2003;125(39):11853\u201365.","journal-title":"J Am Chem Soc"},{"issue":"1","key":"5153_CR74","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1016\/0022-2836(81)90087-5","volume":"147","author":"TF Smith","year":"1981","unstructured":"Smith TF, Waterman MS, et al. Identification of common molecular subsequences. J Mol Biol. 1981;147(1):195\u20137.","journal-title":"J Mol Biol"},{"issue":"1","key":"5153_CR75","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-017-18025-2","volume":"7","author":"F Rayhan","year":"2017","unstructured":"Rayhan F, Ahmed S, Shatabda S, Farid DM, Mousavian Z, Dehzangi A, Rahman MS. idti-esboost: identification of drug target interaction using evolutionary and structural features with boosting. Sci Rep. 2017;7(1):1\u201318.","journal-title":"Sci Rep"},{"issue":"2","key":"5153_CR76","doi-asserted-by":"publisher","first-page":"1004760","DOI":"10.1371\/journal.pcbi.1004760","volume":"12","author":"Y Liu","year":"2016","unstructured":"Liu Y, Wu M, Miao C, Zhao P, Li X-L. Neighborhood regularized logistic matrix factorization for drug\u2013target interaction prediction. PLoS Comput Biol. 2016;12(2):1004760.","journal-title":"PLoS Comput Biol"},{"issue":"2","key":"5153_CR77","doi-asserted-by":"publisher","first-page":"0171839","DOI":"10.1371\/journal.pone.0171839","volume":"12","author":"J Keum","year":"2017","unstructured":"Keum J, Nam H. Self-blm: prediction of drug\u2013target interactions via self-training svm. PLoS ONE. 2017;12(2):0171839.","journal-title":"PLoS ONE"},{"issue":"7","key":"5153_CR78","doi-asserted-by":"publisher","first-page":"3340","DOI":"10.1021\/acs.jcim.9b00408","volume":"59","author":"L-Y Xia","year":"2019","unstructured":"Xia L-Y, Yang Z-Y, Zhang H, Liang Y. Improved prediction of drug\u2013target interactions using self-paced learning with collaborative matrix factorization. J Chem Inf Model. 2019;59(7):3340\u201351.","journal-title":"J Chem Inf Model"},{"issue":"2","key":"5153_CR79","doi-asserted-by":"publisher","first-page":"1604","DOI":"10.1093\/bib\/bbz176","volume":"22","author":"H Luo","year":"2021","unstructured":"Luo H, Li M, Yang M, Wu F-X, Li Y, Wang J. Biomedical data and computational models for drug repositioning: a comprehensive review. Brief Bioinform. 2021;22(2):1604\u201319.","journal-title":"Brief Bioinform"},{"issue":"1\u20132","key":"5153_CR80","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1016\/j.maturitas.2008.11.011","volume":"61","author":"R Sitruk-Ware","year":"2008","unstructured":"Sitruk-Ware R. Reprint of pharmacological profile of progestins. Maturitas. 2008;61(1\u20132):151\u20137.","journal-title":"Maturitas"},{"issue":"8","key":"5153_CR81","doi-asserted-by":"publisher","first-page":"1400","DOI":"10.1038\/sj.npp.1300203","volume":"28","author":"DA Shapiro","year":"2003","unstructured":"Shapiro DA, Renock S, Arrington E, Chiodo LA, Liu L-X, Sibley DR, Roth BL, Mailman R. Aripiprazole, a novel atypical antipsychotic drug with a unique and robust pharmacology. Neuropsychopharmacology. 2003;28(8):1400\u201311.","journal-title":"Neuropsychopharmacology"},{"issue":"1","key":"5153_CR82","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1038\/sj.mp.4002066","volume":"13","author":"H Nasrallah","year":"2008","unstructured":"Nasrallah H. Atypical antipsychotic-induced metabolic side effects: insights from receptor-binding profiles. Mol Psychiatry. 2008;13(1):27\u201335.","journal-title":"Mol Psychiatry"},{"issue":"1","key":"5153_CR83","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1093\/bib\/bbab500","volume":"23","author":"S-H Wang","year":"2022","unstructured":"Wang S-H, Wang C-C, Huang L, Miao L-Y, Chen X. Dual-network collaborative matrix factorization for predicting small molecule-miRNA associations. Brief Bioinform. 2022;23(1):66.","journal-title":"Brief Bioinform"},{"issue":"1","key":"5153_CR84","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1093\/bib\/bbab431","volume":"23","author":"C-C Wang","year":"2022","unstructured":"Wang C-C, Zhu C-C, Chen X. Ensemble of kernel ridge regression-based small molecule-miRNA association prediction in human disease. Brief Bioinform. 2022;23(1):66.","journal-title":"Brief Bioinform"},{"issue":"6","key":"5153_CR85","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1093\/bib\/bbab328","volume":"22","author":"X Chen","year":"2021","unstructured":"Chen X, Zhou C, Wang C-C, Zhao Y. Predicting potential small molecule-miRNA associations based on bounded nuclear norm regularization. Brief Bioinform. 2021;22(6):66.","journal-title":"Brief Bioinform"},{"issue":"6","key":"5153_CR86","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1093\/bib\/bbac468","volume":"23","author":"L Zhang","year":"2022","unstructured":"Zhang L, Wang C-C, Chen X. Predicting drug\u2013target binding affinity through molecule representation block based on multi-head attention and skip connection. Brief Bioinform. 2022;23(6):66.","journal-title":"Brief Bioinform"}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-023-05153-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12859-023-05153-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-023-05153-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,13]],"date-time":"2024-10-13T10:46:46Z","timestamp":1728816406000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/s12859-023-05153-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,3]]},"references-count":86,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["5153"],"URL":"https:\/\/doi.org\/10.1186\/s12859-023-05153-y","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-2129191\/v1","asserted-by":"object"}]},"ISSN":["1471-2105"],"issn-type":[{"value":"1471-2105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,3]]},"assertion":[{"value":"3 October 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 January 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 February 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"38"}}