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Current Computer-Aided Drug Design

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ISSN (Print): 1573-4099
ISSN (Online): 1875-6697

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Review Article

DTIP: A Comparative Analytical Framework for Chemogenomic Drugtarget Interactions Prediction

Author(s): Faraneh Haddadi and Mohammad Reza Kayvanpour*

Volume 17, Issue 1, 2021

Published on: 18 December, 2019

Page: [2 - 21] Pages: 20

DOI: 10.2174/1573409916666191218124520

Price: $65

Abstract

Background: Prediction of drug-target interactions is an essential step in drug discovery. Given drug-target interactions network, the objective of this task is to predict probable missing edges from known interactions. Computationally predicting drug-target interactions is an appropriate alternative for the time-consuming and costly experimental process of drug-target interaction prediction. A large number of computational methods for solving this problem have been proposed in recent years.

Objective: In recent years, several review articles have been published in the field of drug-target interactions prediction. Compared to other review articles, this paper includes a qualitative analysis in the form of a framework, a drug-target interactions prediction (DTIP) framework.

Methods: The framework consists of three sections. Initially, a classification has been presented for drug-target interactions prediction methods based on the link prediction approaches used in these methods. Secondly, general evaluation criteria have been introduced for analyzing approaches. Finally, a qualitative comparison is made between each approach in terms of their advantages and disadvantages.

Results: By providing a new classification of the drug-target interactions prediction approaches and comparing them with the proposed evaluation criteria, this framework provides a convenient and efficient way to select and compare the methods. Moreover, using the framework, we can improve these techniques further.

Conclusion: This paper provides a study to select, compare, and improve chemogenomic drugtarget interactions prediction methods. To this aim, an analytical framework is presented.

Keywords: Chemogenomic, drug-target interactions prediction, drug-target interactions network, machine learning, link prediction, comparative analytical framework, drug discovery.

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[1]
Pean, A.; Naulaerts, S.; Ballester, P.J. Predicting the reliability of drug-target interaction predictions with maximum coverage of target space. Sci. Rep., 2017, 7(1), 3820.
[http://dx.doi.org/10.1038/s41598-017-04264-w] [PMID: 28630414]
[2]
Masoudi-Nejad, A.; Mousavian, Z.; Bozorgmehr, J.H. Drug-target and disease networks: polypharmacology in the post-genomic era. In Silico Pharmacol., 2013, 1(17), 17.
[http://dx.doi.org/10.1186/2193-9616-1-17] [PMID: 25505661]
[3]
Ding, H.; Takigawa, I.; Mamitsuka, H.; Zhu, S. Similarity-based machine learning methods for predicting drug-target interactions: a brief review. Brief. Bioinform., 2014, 15(5), 734-747.
[http://dx.doi.org/10.1093/bib/bbt056] [PMID: 23933754]
[4]
Shi, J-Y.; Yiu, S-M.; Li, Y.; Leung, H.C.M.; Chin, F.Y.L. Predicting drug-target interaction for new drugs using enhanced similarity measures and super-target clustering. Methods, 2015, 83, 98-104.
[http://dx.doi.org/10.1016/j.ymeth.2015.04.036] [PMID: 25957673]
[5]
Jacob, L.; Vert, J-P. Protein-ligand interaction prediction: an improved chemogenomics approach. Bioinformatics, 2008, 24(19), 2149-2156.
[http://dx.doi.org/10.1093/bioinformatics/btn409] [PMID: 18676415]
[6]
Cheng, A.C.; Coleman, R.G.; Smyth, K.T.; Cao, Q.; Soulard, P.; Caffrey, D.R.; Salzberg, A.C.; Huang, E.S. Structure-based maximal affinity model predicts small-molecule drug ability. Nat. Biotechnol., 2007, 25(1), 71-75.
[http://dx.doi.org/10.1038/nbt1273] [PMID: 17211405]
[7]
Ballesteros, J.; Palczewski, K. G protein-coupled receptor drug discovery: implications from the crystal structure of rhodopsin. Curr. Opin. Drug Discov. Devel., 2001, 4(5), 561-574.
[PMID: 12825452]
[8]
Yildirim, M.A.; Goh, K-I.; Cusick, M.E.; Barabasi, A-L.; Vidal, M. Drug-target network. Nat. Biotechnol., 2007, 25(10), 1119-1126.
[http://dx.doi.org/10.1038/nbt1338] [PMID: 17921997]
[9]
Hao, M.; Bryant, S.H.; Wang, Y. Open-source chemogenomic data-driven algorithms for predicting drug-target interactions. Brief. Bioinform., 2019, 20(4), 1465-1474.
[http://dx.doi.org/10.1093/bib/bby010] [PMID: 29420684]
[10]
Yamanishi, Y.; Araki, M.; Gutteridge, A.; Honda, W.; Kanehisa, M. Prediction of drug-target interaction networks from the integration of chemical and genomic spaces. Bioinformatics, 2008, 24(13), i232-i240.
[http://dx.doi.org/10.1093/bioinformatics/btn162] [PMID: 18586719]
[11]
Fakhraei, S.; Raschid, L.; Getoor, L. In Drug-target interaction prediction for drug repurposing with probabilistic similarity logic BioKDD '13 Proceedings of the 12th International Workshop on Data Mining in Bioinformatics, Chicago. 2013, pp. 10-17..
[http://dx.doi.org/d10.1145/2500863.2500870]
[12]
Fu, G.; Ding, Y.; Seal, A.; Chen, B.; Sun, Y.; Bolton, E. Predicting drug target interactions using meta-path-based semantic network analysis. BMC Bioinformatics, 2016, 17(1), 160.
[http://dx.doi.org/10.1186/s12859-016-1005-x] [PMID: 27071755]
[13]
Hakime.; Ö, Elif, O.; Arzucan, Ö.A. A comparative study of SMILES-based compound similarity functions for drug-target interaction prediction. BMC Bioinformatics, 2016, 17, 128.
[http://dx.doi.org/10.1186/s12859-016-0977-x] [PMID: 26987649]
[14]
Xia, Z.; Wu, L-Y.; Zhou, X.; Wong, S.T. Semi-supervised drug-protein interaction prediction from heterogeneous biological spaces. BMC Syst. Biol., 2010, 4(2)(Suppl. 2), S6.
[http://dx.doi.org/10.1186/1752-0509-4-S2-S6] [PMID: 20840733]
[15]
Yamanishi, Y. Chemogenomic approaches to infer drug-target interaction networks. Methods Mol. Biol., 2013, 939, 97-113.
[http://dx.doi.org/10.1007/978-1-62703-107-3_9] [PMID: 23192544]
[16]
Mousavian, Z.; Masoudi-Nejad, A. Drug-target interaction prediction via chemogenomic space: learning-based methods. Expert Opin. Drug Metab. Toxicol., 2014, 10(9), 1273-1287.
[http://dx.doi.org/10.1517/17425255.2014.950222] [PMID: 25112457]
[17]
Le, D-H.; Le, L. Systems pharmacology: a unified framework for prediction of drug-target interactions. Curr. Pharm. Des., 2016, 22(23), 3569-3575.
[http://dx.doi.org/10.2174/1381612822666160418121534] [PMID: 27087598]
[18]
Chen, X.; Yan, C.C.; Zhang, X.; Zhang, X.; Dai, F.; Yin, J.; Zhang, Y. Drug-target interaction prediction: databases, web servers and computational models. Brief. Bioinform., 2016, 17(4), 696-712.
[http://dx.doi.org/10.1093/bib/bbv066] [PMID: 26283676]
[19]
Cheng, T.; Hao, M.; Takeda, T.; Bryant, S.H.; Wang, Y. Large-scale prediction of drug-target interaction: a data-centric review. AAPS J., 2017, 19(5), 1264-1275.
[http://dx.doi.org/10.1208/s12248-017-0092-6] [PMID: 28577120]
[20]
Zhao, Q.; Yu, H.; Ji, M.; Zhao, Y.; Chen, X. Computational model development of drug-target interaction prediction: a review. Curr. Protein Pept. Sci., 2019, 20(6), 492-494.
[http://dx.doi.org/10.2174/1389203720666190123164310] [PMID: 30674253]
[21]
Ezzat, A.; Wu, M.; Li, X.L.; Kwoh, C.K. Computational prediction of drug-target interactions using chemogenomic approaches: an empirical survey. Brief. Bioinform., 2019, 20(4), 1337-1357.
[http://dx.doi.org/10.1093/bib/bby002] [PMID: 29377981]
[22]
Sachdev, K.; Gupta, M.K. A comprehensive review of feature based methods for drug target interaction prediction. J. Biomed. Inform., 2019, 93103159
[http://dx.doi.org/10.1016/j.jbi.2019.103159] [PMID: 30926470]
[23]
Martinez, V. Berzal, F.; Cubero, J.-C. A survey of link prediction in complex networks. ACM Comput. Surv., 2016, 49(4), 33.
[http://dx.doi.org/10.1145/3012704]
[24]
Al Hasan, M.; Zak, J. A survey of link prediction in social networks. Soc. Net. Data Anal., 2011, 1(1), 243-275.
[http://dx.doi.org/10.1007/978-1-4419-8462-3_9]
[25]
Haghani, S.; Keyvanpour, M.R. A systemic analysis of link prediction in social network. Artif. Intell. Rev., 2017, 52(3), 1961-1995.
[http://dx.doi.org/10.1007/s10462-017-9590-2]
[26]
Fakhraei, S.; Huang, B.; Raschid, L.; Getoor, L. Network-based drug-target interaction prediction with probabilistic soft logic. IEEE/ACM Trans. Comput. Biol. Bioinformatics, 2014, 11(5), 775-787.
[http://dx.doi.org/10.1109/TCBB.2014.2325031] [PMID: 26356852]
[27]
Lai, L.; Zhou, T. Link prediction in complex networks: A survey. Physica A, 2011, 390(6), 1150-1170.
[http://dx.doi.org/10.1016/j.physa.2010.11.027]
[28]
Keyvanpour, M.R.; Azizani, F. Classification and analysis of frequent subgraphs mining algorithms. J. Softw., 2012, 7(1), 220-227.
[http://dx.doi.org/10.4304/jsw.7.1.220-227]
[29]
Getoor, L.; Diehl, C.P. In Link mining: a survey. ACM SIGKDD Explorations Newsletter, New York, 2005, 7(2), 3-12.
[http://dx.doi.org/10.1145/1117454.1117456]
[30]
Chen, X.; Liu, M-X.; Yan, G-Y. Drug-target interaction prediction by random walk on the heterogeneous network. Mol. Biosyst., 2012, 8(7), 1970-1978.
[http://dx.doi.org/10.1039/c2mb00002d] [PMID: 22538619]
[31]
Liben-Nowell, D.; Kleinberg, J. The link-prediction problem for social networks. J. Am. Soc. Inf. Sci. Technol., 2007, 58(7), 1019-1031.
[http://dx.doi.org/10.1002/asi.20591]
[32]
Lu, Y.; Guo, Y.; Korhonen, A. Link prediction in drug-target interactions network using similarity indices. BMC Bioinformatics, 2017, 18(1), 39.
[http://dx.doi.org/10.1186/s12859-017-1460-z] [PMID: 28095781]
[33]
Wang, T.; Liao, G. A review of link prediction in social networks. International Conference on Management of e-Commerce and e-Government, Shanghai2014.
[http://dx.doi.org/10.1109/ICMeCG.2014.38]
[34]
Peng, W. BaoWen, X.; YuRong, W.; XiaoYu, Z., Link Prediction in Social Networks: the State-of-the-Art. Sci. China Inf. Sci., 2015, 58(1), 1-38.
[http://dx.doi.org/10.1007/s11432-014-5237-y]
[35]
Barabasi, A.L.; Jeong, H.; Neda, Z.; Ravasz, E. Evolution of the social network of scientific collaborations. Physica A, 2002, 311(3-4), 590-614.
[http://dx.doi.org/10.1016/S0378-4371(02)00736-7]
[36]
Cheng, F.; Liu, C.; Jiang, J.; Lu, W.; Li, W.; Liu, G.; Zhou, W.; Huang, J.; Tang, Y. Prediction of drug-target interactions and drug repositioning via network-based inference. PLOS Comput. Biol., 2012, 8(5)e1002503
[http://dx.doi.org/10.1371/journal.pcbi.1002503] [PMID: 22589709]
[37]
Wu, Z.; Cheng, F.; Li, J.; Li, W.; Liu, G.; Tang, Y. SDTNBI: an integrated network and chemo informatics tool for systematic prediction of drug-target interactions and drug repositioning. Brief. Bioinform., 2017, 18(2), 333-347.
[http://dx.doi.org/10.1093/bib/bbw012] [PMID: 26944082]
[38]
Ba-Alawi, W.; Soufan, O.; Essack, M.; Kalnis, P.; Bajic, V.B. DASPfind: new efficient method to predict drug-target interactions. J. Cheminform., 2016, 8(1), 15.
[http://dx.doi.org/10.1186/s13321-016-0128-4] [PMID: 26985240]
[39]
Yamanishi, Y.; Pauwels, E.; Saigo, H.; Stoven, V. Extracting sets of chemical substructures and protein domains governing drug-target interactions. J. Chem. Inf. Model., 2011, 51(5), 1183-1194.
[http://dx.doi.org/10.1021/ci100476q] [PMID: 21506615]
[40]
Chen, B.; Wild, D.; Guha, R. PubChem as a source of polypharmacology. J. Chem. Inf. Model., 2009, 49(9), 2044-2055.
[http://dx.doi.org/10.1021/ci9001876] [PMID: 19708682]
[41]
Finn, R.D.; Bateman, A.; Clements, J.; Coggill, P.; Eberhardt, R.Y.; Eddy, S.R.; Heger, A.; Hetherington, K.; Holm, L.; Mistry, J.; Sonnhammer, E.L.; Tate, J.; Punta, M. Pfam: the protein families database. Nucleic Acids Res., 2014, 42(Database issue), D222-D230.
[http://dx.doi.org/10.1093/nar/gkt1223] [PMID: 24288371]
[42]
Tabei, Y.; Pauwels, E.; Stoven, V.; Takemoto, K.; Yamanishi, Y. Identification of chemogenomic features from drug-target interaction networks using interpretable classifiers. Bioinformatics, 2012, 28(18), i487-i494.
[http://dx.doi.org/10.1093/bioinformatics/bts412] [PMID: 22962471]
[43]
You, J.; Islam, M.M.; Grenier, L.; Kuang, Q.; McLeod, R.D.; Hu, P. Drug-target interaction network predictions for drug repurposing using LASSO-based regularized linear classification model. Canadian Conference on Artificial Intelligence, 2018, pp. 272-278.
[http://dx.doi.org/10.1007/978-3-319-89656-4_26]
[44]
Yu, H.; Chen, J.; Xu, X.; Li, Y.; Zhao, H.; Fang, Y.; Li, X.; Zhou, W.; Wang, W.; Wang, Y. A systematic prediction of multiple drug-target interactions from chemical, genomic, and pharmacological data. PLoS One, 2012, 7(5)e37608
[http://dx.doi.org/10.1371/journal.pone.0037608] [PMID: 22666371]
[45]
Ezzat, A.; Wu, M.; Li, X-L.; Kwoh, C-K. Drug-target interaction prediction via class imbalance-aware ensemble learning. BMC Bioinformatics, 2016, 17(Suppl. 19), 509.
[http://dx.doi.org/10.1186/s12859-016-1377-y] [PMID: 28155697]
[46]
Cao, D-S.; Xiao, N.; Xu, Q.S.; Chen, A.F. Rcpi: R/Bioconductor package to generate various descriptors of proteins, compounds and their interactions. Bioinformatics, 2015, 31(2), 279-281.
[http://dx.doi.org/10.1093/bioinformatics/btu624] [PMID: 25246429]
[47]
Jinjian, j.; Wang, N.; Chen, P.; Wang, B., DrugECs: An ensemble system with feature subspaces for accurate drug-target interaction prediction. BioMed Res. Int., 2017, 2017(23), 1-10.
[http://dx.doi.org/10.1155/2017/6340316]
[48]
Kawashima, S.; Pokarowski, P.; Pokarowska, M.; Kolinski, A.; Katayama, T.; Kanehisa, M.A. Index: amino acid index database, progress report 2008. Nucleic Acids Res., 2008, 36(Database issue), D202-D205.
[http://dx.doi.org/10.1093/nar/gkm998] [PMID: 17998252]
[49]
Yap, C.W. PaDEL-descriptor: an open source software to calculate molecular descriptors and fingerprints. J. Comput. Chem., 2011, 32(7), 1466-1474.
[http://dx.doi.org/10.1002/jcc.21707] [PMID: 21425294]
[50]
Zhang, J.; Zhu, M.; Chen, P.; Wang, B. DrugRPE: Random projection ensemble approach to drug-target interaction prediction. Neurocomputing, 2017, 228, 256-262.
[http://dx.doi.org/10.1016/j.neucom.2016.10.039]
[51]
Haghani, S.; Keyvanpour, M.R. moLink: modeling link representation of knowledge base. 9th International Conference on Information and Knowledge Technology (IKT 2017), Tehran2017.
[http://dx.doi.org/10.1109/IKT.2017.8258613]
[52]
Haghani, S.; Keyvanpour, M.R. Temporal link prediction: techniques and challenges. Computer science and information technologies. Yerevan, ; , 2017.
[53]
Wang, W.; Chen, X.; Jiao, P.; Jin, D. Similarity-based regularized latent feature model for link prediction in bipartite networks. Sci. Rep., 2017, 7(1), 16996.
[http://dx.doi.org/10.1038/s41598-017-17157-9] [PMID: 29208988]
[54]
Wang, C.; Kurgan, L. Review and comparative assessment of similarity-based methods for prediction of drug-protein interactions in the druggable human proteome. Brief. Bioinform., 2019, 20(6), 2066-2087.
[http://dx.doi.org/10.1093/bib/bby069] [PMID: 30102367]
[55]
Ganen, M. Predicting drug-target interactions from chemical and genomic kernels using Bayesian matrix factorization. Bioinformatics, 2012, 28(18), 2304-2310.
[http://dx.doi.org/10.1093/bioinformatics/bts360] [PMID: 22730431]
[56]
Zheng, X.; Ding, H.; Mamitsuka, H.; Zhu, S. Collaborative matrix factorization with multiple similarities for predicting drug-target interactions. KDD (tm)13 Proceedings of the 19th ACM SIGKDD International Conference On Knowledge Discovery And Data Mining, Chicago 2013, pp. 1025-1033..
[http://dx.doi.org/10.1145/2487575.2487670]
[57]
Ezzat, A.; Zhao, P.; Wu, M.; Li, X-L.; Kwoh, C-K. Drug-target interaction prediction with graph regularized matrix factorization. IEEE/ACM Trans. Comput. Biol. Bioinformatics, 2017, 14(3), 646-656.
[http://dx.doi.org/10.1109/TCBB.2016.2530062] [PMID: 26890921]
[58]
Hao, M.; Bryant, S.H.; Wang, Y. Predicting drug-target interactions by dual-network integrated logistic matrix factorization. Sci. Rep., 2017, 7, 40376.
[http://dx.doi.org/10.1038/srep40376] [PMID: 28079135]
[59]
van Laarhoven, T.; Nabuurs, S.B.; Marchiori, E. Gaussian interaction profile kernels for predicting drug-target interaction. Bioinformatics, 2011, 27(21), 3036-3043.
[http://dx.doi.org/10.1093/bioinformatics/btr500] [PMID: 21893517]
[60]
van Laarhoven, T.; Marchiori, E. Predicting drug-target interactions for new drug compounds using a weighted nearest neighbor profile. PLoS One, 2013, 8(6)e66952
[http://dx.doi.org/10.1371/journal.pone.0066952] [PMID: 23840562]
[61]
Bleakley, K.; Yamanishi, Y. Supervised prediction of drug-target interactions using bipartite local models. Bioinformatics, 2009, 25(18), 2397-2403.
[http://dx.doi.org/10.1093/bioinformatics/btp433] [PMID: 19605421]
[62]
Keum, J.; Nam, H. SELF-BLM: Prediction of drug-target interactions via self-training SVM. PLoS One, 2017, 12(2)e0171839
[http://dx.doi.org/10.1371/journal.pone.0171839] [PMID: 28192537]
[63]
Du, Y.; Wang, J.; Wang, X.; Chen, J.; Chang, H. Predicting drug-target interaction via wide and deep learning. Proceedings of the 2018 6th International Conference on Bioinformatics and Computational Biology, Chengdu2018.
[http://dx.doi.org/10.1145/3194480.3194491]
[64]
LeCun, Y.; Bengio, Y.; Hinton, G. Deep learning. Nature, 2015, 521(7553), 436-444.
[http://dx.doi.org/10.1038/nature14539]
[65]
Schmidhuber, J. Deep learning in neural networks: an overview. Neural Netw., 2015, 61, 85-117.
[http://dx.doi.org/10.1016/j.neunet.2014.09.003] [PMID: 25462637]
[66]
Feng, Q.; Dueva, E.; Cherkasov, A.; Ester, M. PADME: a deep learning-based framework for drug-target interaction prediction., (Preprint) arXiv, 2018.
[67]
Wang, Y.; Zeng, J. Predicting drug-target interactions using restricted Boltzmann machines. Bioinformatics, 2013, 29(13), i126-i134.
[http://dx.doi.org/10.1093/bioinformatics/btt234] [PMID: 23812976]
[68]
Wen, M.; Zhang, Z.; Niu, S.; Sha, H.; Yang, R.; Yun, Y.; Lu, H. Deep learning-based drug-target interaction prediction. J. Proteome Res., 2017, 16(4), 1401-1409.
[http://dx.doi.org/10.1021/acs.jproteome.6b00618] [PMID: 28264154]
[69]
Tian, K.; Shao, M.; Wang, Y.; Guan, J.; Zhou, S. Boosting compound-protein interaction prediction by deep learning. Methods, 2016, 110, 64-72.
[http://dx.doi.org/10.1016/j.ymeth.2016.06.024] [PMID: 27378654]
[70]
Kuhn, M.; Szklarczyk, D.; Pletscher-Frankild, S.; Blicher, T.H.; von Mering, C.; Jensen, L.J.; Bork, P. STITCH 4: integration of protein-chemical interactions with user data. Nucleic Acids Res., 2014, 42(Database issue), D401-D407.
[http://dx.doi.org/10.1093/nar/gkt1207] [PMID: 24293645]
[71]
Wang, Y.; Xiao, J.; Suzek, T.O.; Zhang, J.; Wang, J.; Bryant, S.H. PubChem: a public information system for analyzing bioactivities of small molecules. Nucleic Acids Res., 2009, 37, W623-W633.
[http://dx.doi.org/10.1093/nar/gkp456]
[72]
Hakime, Ö.; Arzucan, Ö.; Elif, O. 6DeepDTA: deep drug-target binding affinity prediction. Bioinformatics, 2018, 34(17), i821-i829.
[http://dx.doi.org/10.1093/bioinformatics/bty593] [PMID: 30423097]
[73]
Luo, Y.; Zhao, X.; Zhou, J.; Yang, J.; Zhang, Y.; Kuang, W.; Peng, J.; Chen, L.; Zeng, J. A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information. Nat. Commun., 2017, 8(1), 573.
[http://dx.doi.org/10.1038/s41467-017-00680-8] [PMID: 28924171]
[74]
Sadeghi, S.S.; Keyvanpour, M.R. Computational drug repurposing: research opportunities and challenges classification. Curr. Comput. Aided Drug Des., 2019, 15(1), 354-364.
[http://dx.doi.org/10.2174/1573409915666190613113822] [PMID: 31198115]
[75]
Yang, Y.; Lichtenwalter, R.N.; Chawla, N.V. Evaluating link prediction methods. Knowl. Inf. Syst., 2015, 45(3), 751-782.
[http://dx.doi.org/10.1007/s10115-014-0789-0]
[76]
Davis, J.; Goadrich, M. The relationship between precision-recall and ROC curves. Proceedings of the 23rd International Conference on Machine Learning, Pittsburgh2006, pp. 233-240.
[http://dx.doi.org/10.1145/1143844.1143874]
[77]
Saeys, Y.; Inza, I.; Larrañaga, P. A review of feature selection techniques in bioinformatics. Bioinformatics, 2007, 23(19), 2507-2517.
[http://dx.doi.org/10.1093/bioinformatics/btm344] [PMID: 17720704]
[78]
Bengio, Y.; Courville, A.; Vincent, P. Representation learning: a review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell., 2013, 35(8), 1798-1828.
[http://dx.doi.org/10.1109/TPAMI.2013.50] [PMID: 23787338]
[79]
Cobanoglu, M.C.; Liu, C.; Hu, F.; Oltvai, Z.N.; Bahar, I. Predicting drug-target interactions using probabilistic matrix factorization. J. Chem. Inf. Model., 2013, 53(12), 3399-3409.
[http://dx.doi.org/10.1021/ci400219z] [PMID: 24289468]
[80]
LeDell, E.E. Scalable Ensemble Learning and Computationally Efficient Variance Estimation.PhD thesis University of California, Berkeley,, 2015.
[81]
Sze, V.; Chen, Y-H.; Yang, T-J.; Emer, J.S. Efficient processing of deep neural networks: a tutorial and survey. Proceedings of the IEEE, 2017.
[http://dx.doi.org/10.1109/JPROC.2017.2761740]
[82]
He, H.; Garcia, E.A. Learning from imbalanced data. IEEE Trans. Knowl. Data Eng., 2009, 21(9), 1263-1284.
[http://dx.doi.org/10.1109/TKDE.2008.239]
[83]
Kotsiantis, S.; Kanellopoulos, D.; Pintelas, P. Handling imbalanced datasets: A review. GESTS International Transactions on Computer Science and Engineering, 2006, 30(1), 25-36.
[84]
Wallach, I.; Dzamba, M.; Heifets, A. AtomNet: a deep convolutional neural network for bioactivity prediction in structure-based drug discovery, (Preprint) arXiv, 2015.
[85]
Lee, I.; Keum, J.; Nam, H. DeepConv-DTI: prediction of drug-target interactions via deep learning with convolution on protein sequences. PLOS Comput. Biol., 2019, 15(6)e1007129
[http://dx.doi.org/10.1371/journal.pcbi.1007129] [PMID: 31199797]
[86]
Ding, Y.; Tang, J.; Guo, F. Identification of drug-target interactions via multiple information integration. Inf. Sci., 2017, 418-419, 546-560.
[http://dx.doi.org/10.1016/j.ins.2017.08.045]
[87]
Wang, Y-C.; Zhang, C-H.; Deng, N-Y.; Wang, Y. Kernel-based data fusion improves the drug-protein interaction prediction. Comput. Biol. Chem., 2011, 35(6), 353-362.
[http://dx.doi.org/10.1016/j.compbiolchem.2011.10.003] [PMID: 22099632]
[88]
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), 11853-11865.
[http://dx.doi.org/10.1021/ja036030u] [PMID: 14505407]
[89]
Smith, T.F.; Waterman, M.S. Identification of common molecular subsequences. J. Mol. Biol., 1981, 147(1), 195-197.
[http://dx.doi.org/10.1016/0022-2836(81)90087-5] [PMID: 7265238]
[90]
Haddadi, F.; Keyvanpour, M. LINGOBLM: using LINGO kernel in bipartite local model. 5th Conference on Knowledge Based Engineering and Innovation (KBEI), Tehran2019.
[http://dx.doi.org/10.1109/KBEI.2019.8734956]
[91]
Vidal, D.; Thormann, M.; Pons, M. LINGO, an efficient holographic text based method to calculate biophysical properties and intermolecular similarities. J. Chem. Inf. Model., 2005, 45(2), 386-393.
[http://dx.doi.org/10.1021/ci0496797] [PMID: 15807504]
[92]
Chen, H.; Zhang, Z. A semi-supervised method for drug-target interaction prediction with consistency in networks. PLoS One, 2013, 8(5)e62975
[http://dx.doi.org/10.1371/journal.pone.0062975] [PMID: 23667553]
[93]
Gu, Q.; Ding, Y.; Zhang, T.; Han, T. Prediction drug-target interaction networks based on semi-supervised learning method. 35th Chinese Control Conference (CCC), Chengdu2016.
[http://dx.doi.org/10.1109/ChiCC.2016.7554493]
[94]
Peng, L.; Zhu, W.; Liao, B.; Duan, Y.; Chen, M.; Chen, Y.; Yang, J. Screening drug-target interactions with positive-unlabeled learning. Sci. Rep., 2017, 7(1), 8087.
[http://dx.doi.org/10.1038/s41598-017-08079-7] [PMID: 28808275]
[95]
Wang, Y.; Bryant, S.H.; Cheng, T.; Wang, J.; Gindulyte, A.; Shoemaker, B.A.; Thiessen, P.A.; He, S.; Zhang, J. PubChem BioAssay: 2017 update. Nucleic Acids Res., 2017, 45(D1), D955-D963.
[http://dx.doi.org/10.1093/nar/gkw1118] [PMID: 27899599]
[96]
Harnie, D.; Mathijs, S.; Vapirev, A.E.; Wegner, J.K. Scaling machine learning for target prediction in drug discovery using Apache Spark. Future Gener. Comput. Syst., 2016, 67, 409-441.
[http://dx.doi.org/10.1016/j.future.2016.04.023]

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