d0c30966-61c6-442f-b9f3-88333d04c5d7 beie director@blueeyesintelligence.org 10.35940/ijrte.E5205.019521 10.1093/bib/bbr013J.T. Dudley, T. Deshpande, A.J Butte, "Exploring drug disease relationships for computational drug repositioning", Briefings Bioinf. 12(4) (2011) 303-31110.1093/bib/bby002A. Ezzat, et.al., "Computational prediction of drug- target interactions using chemogenomic approaches: an empirical survey", Briefings Bioinf. (2018)L. C. Wang, Z. X. Yang, Y. Wang, N.Y. Deng, "Computationally probingdrug-protein interactions via support vector machine", Lett. Drug DesDiscov. 7 (2010) 370-37810.1089/cmb.2017.0135J. Wang, Z.H. You, X. Chen, S. X. Xia, F. Liu, X. Yan, Y. Zhou, K. J. Song, "A computational-based method for predicting drug-target interactions by using stacked auto encoder deep neural network", J.Comput.Biol.25(2017)361-373.H. Yu, J. Chen, X. Xu, Y. Li, H. Zhao, Y. Fang, X. Li, W. Zhou, W. Wang,Y. Wang, "A systematic prediction of multiple drug-target interactions10.1371/journal.pone.0037608from chemical, genomic, and pharmacological data," PLoS One7(5)(2012)e37608.Chih. -Fong Tsaia, Wei-Chao Lin, Ya-Han Hue, Guan-Ting Yao, "Under10.1016/j.ins.2018.10.029sampling class imbalanced datasets by combining clustering analysis and instance selection" (2019) 47-54.K. Bleakley, Y. Yamanishi, "Supervised prediction of drug- target10.1093/bioinformatics/btp433interactions using bipartite local models", 25(18) (2009)2397-2403.10.1371/journal.pcbi.1002503F. Cheng, C. Liu, J. Jiang, W. Lu, W. Li, G. Liu, W. Zhou, J. Huang, Y. Tang, "Prediction of drug-target interactions and drug repositioning via network-based inference", PLoS Comput.Biol.8(5)(2012)e1002503.10.1016/j.vascn.2015.11.002Z Mousavian, S Khakabimamaghani, K Kavousi, A Masoudi-Nejad "Drug-target interaction prediction from PSSM based evolutionary information" J. Pharn. Toxicol. Methods 78(2016)42-51.10.3390/molecules22071119F. R. Meng, Z. H. You, X. Chen, Y. Zhou, J.Y, "An prediction of drug-target interaction networks from the integration of protein sequences and drug chemical structures", Molecules 22(2017)1119.10.1093/bioinformatics/btx731R. S. Olayan, H. Ashoor, V. B. Bajic, "DDR:efficient computational method to predict drug-target interactions using graph mining and machine learning approaches", Bioinformatics 34(7)(2018)1164-1173D. T. Jones, "Protein secondary structure prediction based on positionMol Biolscoring matrices"292195199910.1006/jmbi.1999.3091specific scoring matrices",J.Mol.Biol.292(1999)195-202.S. F.Altschul, T.L.Madden, A.A.Scha¨ffer, J. Zhang, W. Miller, D.J. Lipman, "Gapped BLAST and PSI-BLAST: a new generation of protein10.1093/nar/25.17.3389database search programs, Nucleic Acids".25(1997)3389-3402.10.1021/ci100476qY. Yamanishi, E. Pauwels, H. Saigo, V. Stoven, "Extracting sets of chemical substructures and protein domains governing drug-target interactions" J.Chem. Inform.Model.51(2011)1183-1194.10.1186/1758-2946-3-33M. O. Boyle, M. Banck, C. A. James, C. Morley, T. Vandermeersch, G. R. Hutchison, "Open Babel: an open chemical tool box interactions" form.3(2011)33."J R Statics Soc BTibshirani73273201110.1111/j.1467-9868.2011.00771.xR. Tibshirani, "Regression shrinkage and selection via the LASSO:"J.R Statics. Soc. B73(2011)273-283Information SciencesTao48731201910.1016/j.ins.2019.02.062Xinmin Tao, Qing Li, Wenjie Guo, Chao ren, Chenxi Li, Rui Liu, Junrong "Self-adaptive cost weights-based support vector machine cost-sensitive ensemble for imbalanced data classification", Information Sciences 487(2019),31-56.J. Zhong, Y. Sun, W. Peng, M. Xie, J. Yang, and X. Tang, "XGBFEMF:10.1109/TNB.2018.2842219An XGBoost-based framework for essential protein prediction,", IEEE Trans. Nanobiosci., vol. 17, no. 3, pp. 243-250, Jul. 2018.