Deep Learning in the Study of Protein-Related Interactions

Author(s): Cheng Shi, Jiaxing Chen, Xinyue Kang, Guiling Zhao, Xingzhen Lao*, Heng Zheng*

Journal Name: Protein & Peptide Letters

Volume 27 , Issue 5 , 2020

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Graphical Abstract:


Protein-related interaction prediction is critical to understanding life processes, biological functions, and mechanisms of drug action. Experimental methods used to determine proteinrelated interactions have always been costly and inefficient. In recent years, advances in biological and medical technology have provided us with explosive biological and physiological data, and deep learning-based algorithms have shown great promise in extracting features and learning patterns from complex data. At present, deep learning in protein research has emerged. In this review, we provide an introductory overview of the deep neural network theory and its unique properties. Mainly focused on the application of this technology in protein-related interactions prediction over the past five years, including protein-protein interactions prediction, protein-RNA\DNA, Protein– drug interactions prediction, and others. Finally, we discuss some of the challenges that deep learning currently faces.

Keywords: Protein interactions, protein-RNA/DNA interactions, deep learning, machine learning, computational biology, Protein- drug interactions.

Saurin, A.J.; Delfini, M.C.; Maurel-Zaffran, C.; Graba, Y. The generic facet of hox protein function. Trends Genet., 2018, 34(12), 941-953.
[] [PMID: 30241969]
Fernandez-Funez, P.; Sanchez-Garcia, J.; Rincon-Limas, D.E. Drosophila models of prionopathies: insight into prion protein function, transmission, and neurotoxicity. Curr. Opin. Genet. Dev., 2017, 44, 141-148.
[] [PMID: 28415023]
Babu, M.M. The contribution of intrinsically disordered regions to protein function, cellular complexity, and human disease. Biochem. Soc. Trans., 2016, 44(5), 1185-1200.
[] [PMID: 27911701]
Berezovsky, I.N.; Guarnera, E.; Zheng, Z.; Eisenhaber, B.; Eisenhaber, F. Protein function machinery: from basic structural units to modulation of activity. Curr. Opin. Struct. Biol., 2017, 42, 67-74.
[] [PMID: 27865209]
Guglielmi, G.; Falk, H.J.; De Renzis, S. Optogenetic control of protein function: From intracellular processes to tissue morphogenesis. Trends Cell Biol., 2016, 26(11), 864-874.
[] [PMID: 27727011]
Berggård, T.; Linse, S.; James, P. Methods for the detection and analysis of protein-protein interactions. Proteomics, 2007, 7(16), 2833-2842.
[] [PMID: 17640003]
Spiltoir, J.I.; Tucker, C.L. Photodimerization systems for regulating protein-protein interactions with light. Curr. Opin. Struct. Biol., 2019, 57, 1-8.
[] [PMID: 30818200]
Kosol, S.; Jenner, M.; Lewandowski, J.R.; Challis, G.L. Protein-protein interactions in trans-AT polyketide synthases. Nat. Prod. Rep., 2018, 35(10), 1097-1109.
[] [PMID: 30280735]
Sierecki, E. The Mediator complex and the role of protein-protein interactions in the gene regulation machinery. Semin. Cell Dev. Biol., 2018, S1084-9521(17), 30392-30390.
[] [PMID: 30278226]
Kim, S.; Thiessen, P.A.; Bolton, E.E.; Chen, J.; Fu, G.; Gindulyte, A.; Han, L.; He, J.; He, S.; Shoemaker, B.A.; Wang, J.; Yu, B.; Zhang, J.; Bryant, S.H. PubChem substance and compound databases. Nucleic Acids Res., 2016, 44(D1), D1202-D1213.
[] [PMID: 26400175]
Cios, K.J.; Mamitsuka, H.; Nagashima, T.; Tadeusiewicz, R. Computational intelligence in solving bioinformatics problems. Artif. Intell. Med., 2005, 35(1-2), 1-8.
[] [PMID: 16095889]
Papadatos, G.; Gaulton, A.; Hersey, A.; Overington, J.P. Activity, assay and target data curation and quality in the ChEMBL database. J. Comput. Aided Mol. Des., 2015, 29(9), 885-896.
[] [PMID: 26201396]
Chen, X.; Wang, L.; Qu, J.; Guan, N.N.; Li, J.Q. Predicting miRNA-disease association based on inductive matrix completion. Bioinformatics, 2018, 34(24), 4256-4265.
[] [PMID: 29939227]
Zhao, Q.; Zhang, Y.; Hu, H.; Ren, G.; Zhang, W.; Liu, H. IRWNRLPI: Integrating random walk and neighborhood regularized logistic matrix factorization for lncRNA-protein interaction prediction. Front. Genet., 2018, 9, 239.
[] [PMID: 30023002]
Chen, X.; Xie, D.; Wang, L.; Zhao, Q.; You, Z.H.; Liu, H. BNPMDA: Bipartite Network Projection for MiRNA-Disease Association prediction. Bioinformatics, 2018, 34(18), 3178-3186.
[] [PMID: 29701758]
Chen, X.; Yin, J.; Qu, J.; Huang, L. MDHGI: Matrix Decomposition and Heterogeneous Graph Inference for miRNA-disease association prediction. PLOS Comput. Biol., 2018, 14(8), e1006418
[] [PMID: 30142158]
Cortes, C.; Vapnik, V. Support-vector networks. Mach. Learn., 1995, 20(3), 273-297.
Ho, T.K. In: Random decision forests. Proceedings of 3rd International Conference on Document Analysis and Recognition, Montreal, Quebec, Canada, 1995, pp. 278-282.
Salt, D.W. The use of artificial neural networks in QSAR. Pestic. Sci., 1992, 36, 161-170.
Zhao, Q.; Yu, H.; Ming, Z.; Hu, H.; Ren, G.; Liu, H. The bipartite network projection-recommended algorithm for predicting long non-coding RNA-protein interactions. Mol. Ther. Nucleic Acids, 2018, 13, 464-471.
[] [PMID: 30388620]
He, B.; Kang, J.; Ru, B.; Ding, H.; Zhou, P.; Huang, J. SABinder: A web service for predicting streptavidin-binding peptides. BioMed Res. Int., 2016, 2016, 9175143
[] [PMID: 27610387]
Tang, Q.; Nie, F.; Kang, J.; Ding, H.; Zhou, P.; Huang, J. NIEluter: Predicting peptides eluted from HLA class I molecules. J. Immunol. Methods, 2015, 422, 22-27.
[] [PMID: 25862605]
Hu, H.; Zhang, L.; Ai, H.; Zhang, H.; Fan, Y.; Zhao, Q.; Liu, H. HLPI-Ensemble: Prediction of human lncRNA-protein interactions based on ensemble strategy. RNA Biol., 2018, 15(6), 797-806.
[] [PMID: 29583068]
Hu, H.; Zhu, C.; Ai, H.; Zhang, L.; Zhao, J.; Zhao, Q.; Liu, H. LPI-ETSLP: lncRNA-protein interaction prediction using eigenvalue transformation-based semi-supervised link prediction. Mol. Biosyst., 2017, 13(9), 1781-1787.
[] [PMID: 28702594]
Gabel, J.; Desaphy, J.; Rognan, D. Beware of machine learning-based scoring functions-on the danger of developing black boxes. J. Chem. Inf. Model., 2014, 54(10), 2807-2815.
[] [PMID: 25207678]
Weigel, K.A.; VanRaden, P.M.; Norman, H.D.; Grosu, H. A 100-Year Review: Methods and impact of genetic selection in dairy cattle-From daughter-dam comparisons to deep learning algorithms. J. Dairy Sci., 2017, 100(12), 10234-10250.
[] [PMID: 29153163]
Mayo, R.C.; Leung, J. Artificial intelligence and deep learning - Radiology’s next frontier? Clin. Imaging, 2018, 49, 87-88.
[] [PMID: 29161580]
LeCun, Y.; Bengio, Y.; Hinton, G. Deep learning. Nature, 2015, 521(7553), 436-444.
[] [PMID: 26017442]
Reichstein, M.; Camps-Valls, G.; Stevens, B.; Jung, M.; Denzler, J.; Carvalhais, N.; Prabhat, Deep learning and process understanding for data-driven Earth system science. Nature, 2019, 566(7743), 195-204.
[] [PMID: 30760912]
Akkus, Z.; Galimzianova, A.; Hoogi, A.; Rubin, D.L.; Erickson, B.J. Deep learning for brain MRI segmentation: State of the art and future directions. J. Digit. Imaging, 2017, 30(4), 449-459.
[] [PMID: 28577131]
Nketia, T.A.; Sailem, H.; Rohde, G.; Machiraju, R.; Rittscher, J. Analysis of live cell images: Methods, tools and opportunities. Methods, 2017, 115, 65-79.
[] [PMID: 28242295]
Krizhevsky, A.; Sutskever, I.; Hinton, G.E. Imagenet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst., 2012, 25(2), 1097-1105.
Kim, S.G.; Harwani, M.; Grama, A.; Chaterji, S. EP-DNN: A deep neural network-based global enhancer prediction algorithm. Sci. Rep., 2016, 6, 38433.
[] [PMID: 27929098]
Ji, S.; Yang, M.; Yu, K. 3D convolutional neural networks for human action recognition. IEEE Trans. Pattern Anal. Mach. Intell., 2013, 35(1), 221-231.
Abdelbar, A.M.; Andrews, E.A.; Wunsch, D.C., II Abductive reasoning with recurrent neural networks. Neural Netw., 2003, 16(5-6), 665-673.
[] [PMID: 12850021]
Simonyan, K.; Zisserman, A. Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556, 2014,
Hinton, G.; Deng, L.; Yu, D. Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. IEEE Signal Process. Mag., 2012, 29(6), 82-97.
Kadurin, A.; Nikolenko, S.; Khrabrov, K.; Aliper, A.; Zhavoronkov, A. druGAN: An advanced generative adversarial autoencoder model for de novo generation of new molecules with desired molecular properties in silico. Mol. Pharm., 2017, 14(9), 3098-3104.
[] [PMID: 28703000]
Lusci, A.; Pollastri, G.; Baldi, P. Deep architectures and deep learning in chemoinformatics: the prediction of aqueous solubility for drug-like molecules. J. Chem. Inf. Model., 2013, 53(7), 1563-1575.
[] [PMID: 23795551]
Ferrè, F.; Colantoni, A.; Helmer-Citterich, M. Revealing protein-lncRNA interaction. Brief. Bioinform., 2016, 17(1), 106-116.
[] [PMID: 26041786]
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., 2018. [Epub Ahead of Print]
[] [PMID: 30325405]
Chen, X.; Xie, D.; Zhao, Q.; You, Z.H. MicroRNAs and complex diseases: from experimental results to computational models. Brief. Bioinform., 2019, 20(2), 515-539.
[] [PMID: 29045685]
Chen, X.; Yan, C.C.; Zhang, X.; You, Z.H. Long non-coding RNAs and complex diseases: from experimental results to computational models. Brief. Bioinform., 2017, 18(4), 558-576.
[] [PMID: 27345524]
Chen, X.; Yan, G.Y. Novel human lncRNA-disease association inference based on lncRNA expression profiles. Bioinformatics, 2013, 29(20), 2617-2624.
[] [PMID: 24002109]
Ray, D.; Kazan, H.; Chan, E.T.; Peña Castillo, L.; Chaudhry, S.; Talukder, S.; Blencowe, B.J.; Morris, Q.; Hughes, T.R. Rapid and systematic analysis of the RNA recognition specificities of RNA-binding proteins. Nat. Biotechnol., 2009, 27(7), 667-670.
[] [PMID: 19561594]
Hafner, M.; Landthaler, M.; Burger, L.; Khorshid, M.; Hausser, J.; Berninger, P.; Rothballer, A.; Ascano, M., Jr; Jungkamp, A.C.; Munschauer, M.; Ulrich, A.; Wardle, G.S.; Dewell, S.; Zavolan, M.; Tuschl, T. Transcriptome-wide identification of RNA-binding protein and microRNA target sites by PAR-CLIP. Cell, 2010, 141(1), 129-141.
[] [PMID: 20371350]
Zhao, Q.; Liang, D.; Hu, H.; Ren, G.; Liu, H. RWLPAP: Random walk for lncRNA-protein associations prediction. Protein Pept. Lett., 2018, 25(9), 830-837.
[] [PMID: 30182833]
Chen, X.; Huang, L. LRSSLMDA: Laplacian Regularized Sparse Subspace Learning for MiRNA-Disease Association prediction. PLOS Comput. Biol., 2017, 13(12), e1005912
[] [PMID: 29253885]
You, Z.H.; Huang, Z.A.; Zhu, Z.; Yan, G.Y.; Li, Z.W.; Wen, Z.; Chen, X. PBMDA: A novel and effective path-based computational model for miRNA-disease association prediction. PLOS Comput. Biol., 2017, 13(3), e1005455
[] [PMID: 28339468]
Alipanahi, B.; Delong, A.; Weirauch, M.T.; Frey, B.J. Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning. Nat. Biotechnol., 2015, 33(8), 831-838.
[] [PMID: 26213851]
Zeng, H.; Edwards, M.D.; Liu, G.; Gifford, D.K. Convolutional neural network architectures for predicting DNA-protein binding. Bioinformatics, 2016, 32(12), i121-i127.
[] [PMID: 27307608]
Wang, L.; Yan, X.; Liu, M.L.; Song, K.J.; Sun, X.F.; Pan, W.W. Prediction of RNA-protein interactions by combining deep convolutional neural network with feature selection ensemble method. J. Theor. Biol., 2019, 461, 230-238.
[] [PMID: 30321541]
Zhang, S.; Zhou, J.; Hu, H.; Gong, H.; Chen, L.; Cheng, C.; Zeng, J. A deep learning framework for modeling structural features of RNA-binding protein targets. Nucleic Acids Res., 2016, 44(4), e32
[] [PMID: 26467480]
Pan, X.; Fan, Y.X.; Yan, J.; Shen, H.B. IPMiner: hidden ncRNA-protein interaction sequential pattern mining with stacked autoencoder for accurate computational prediction. BMC Genomics, 2016, 17, 582.
[] [PMID: 27506469]
Pan, X.; Shen, H.B. RNA-protein binding motifs mining with a new hybrid deep learning based cross-domain knowledge integration approach. BMC Bioinformatics, 2017, 18, 136.
Pan, X. Prediction of RNA-protein sequence and structure binding preferences using deep convolutional and recurrent neural networks. BMC Genomics, 2018, 19, 511.
Li, S.; Dong, F.; Wu, Y.; Zhang, S.; Zhang, C.; Liu, X.; Jiang, T.; Zeng, J. A deep boosting based approach for capturing the sequence binding preferences of RNA-binding proteins from high-throughput CLIP-seq data. Nucleic Acids Res., 2017, 45(14), e129
[] [PMID: 28575488]
Pan, X.; Shen, H.B. Learning distributed representations of RNA sequences and its application for predicting RNA-protein binding sites with a convolutional neural network. Neurocomputing, 2018, 305, 51-58.
Yang, C.; Yang, L.; Zhou, M.; Xie, H.; Zhang, C.; Wang, M.D.; Zhu, H. LncADeep: an ab initio lncRNA identification and functional annotation tool based on deep learning. Bioinformatics, 2018, 34(22), 3825-3834.
[] [PMID: 29850816]
Hill, S.T.; Kuintzle, R.; Teegarden, A.; Merrill, E., III; Danaee, P.; Hendrix, D.A. A deep recurrent neural network discovers complex biological rules to decipher RNA protein-coding potential. Nucleic Acids Res., 2018, 46(16), 8105-8113.
[] [PMID: 29986088]
Kumar, M.; Gromiha, M.M.; Raghava, G.P.S. Identification of DNA-binding proteins using support vector machines and evolutionary profiles. BMC Bioinformatics, 2007, 8, 463.
[] [PMID: 18042272]
Zhou, J.; Troyanskaya, O.G. Predicting effects of noncoding variants with deep learning-based sequence model. Nat. Methods, 2015, 12(10), 931-934.
[] [PMID: 26301843]
Kelley, D.R.; Snoek, J.; Rinn, J.L. Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks. Genome Res., 2016, 26(7), 990-999.
[] [PMID: 27197224]
Qu, Y-H.; Yu, H.; Gong, X-J.; Xu, J-H.; Lee, H.S. On the prediction of DNA-binding proteins only from primary sequences: A deep learning approach. PLoS One, 2017, 12(12), e0188129
[] [PMID: 29287069]
Pazos, F.; Valencia, A. In silico two-hybrid system for the selection of physically interacting protein pairs. Proteins, 2002, 47(2), 219-227.
Gavin, A.C.; Bösche, M.; Krause, R.; Grandi, P.; Marzioch, M.; Bauer, A.; Schultz, J.; Rick, J.M.; Michon, A.M.; Cruciat, C.M.; Remor, M.; Höfert, C.; Schelder, M.; Brajenovic, M.; Ruffner, H.; Merino, A.; Klein, K.; Hudak, M.; Dickson, D.; Rudi, T.; Gnau, V.; Bauch, A.; Bastuck, S.; Huhse, B.; Leutwein, C.; Heurtier, M.A.; Copley, R.R.; Edelmann, A.; Querfurth, E.; Rybin, V.; Drewes, G.; Raida, M.; Bouwmeester, T.; Bork, P.; Seraphin, B.; Kuster, B.; Neubauer, G.; Superti-Furga, G. Functional organization of the yeast proteome by systematic analysis of protein complexes. Nature, 2002, 415(6868), 141-147.
[] [PMID: 11805826]
Yang, X.; Coulombe-Huntington, J.; Kang, S.; Sheynkman, G.M.; Hao, T.; Richardson, A.; Sun, S.; Yang, F.; Shen, Y.A.; Murray, R.R.; Spirohn, K.; Begg, B.E.; Duran-Frigola, M.; MacWilliams, A.; Pevzner, S.J.; Zhong, Q.; Trigg, S.A.; Tam, S.; Ghamsari, L.; Sahni, N.; Yi, S.; Rodriguez, M.D.; Balcha, D.; Tan, G.; Costanzo, M.; Andrews, B.; Boone, C.; Zhou, X.J.; Salehi-Ashtiani, K.; Charloteaux, B.; Chen, A.A.; Calderwood, M.A.; Aloy, P.; Roth, F.P.; Hill, D.E.; Iakoucheva, L.M.; Xia, Y.; Vidal, M. Widespread expansion of protein interaction capabilities by alternative splicing. Cell, 2016, 164(4), 805-817.
[] [PMID: 26871637]
Sun, T.; Zhou, B.; Lai, L.; Pei, J. Sequence-based prediction of protein protein interaction using a deep-learning algorithm. BMC Bioinformatics, 2017, 18(1), 277.
[] [PMID: 28545462]
Du, X.; Sun, S.; Hu, C.; Yao, Y.; Yan, Y.; Zhang, Y. DeepPPI: Boosting prediction of protein-protein interactions with deep neural networks. J. Chem. Inf. Model., 2017, 57(6), 1499-1510.
[] [PMID: 28514151]
Wang, J.; Zhang, L.; Jia, L.; Ren, Y.; Yu, G. Protein-protein interactions prediction using a novel local conjoint triad descriptor of amino acid sequences. Int. J. Mol. Sci., 2017, 18(11), e2373
[] [PMID: 29117139]
Li, H.; Gong, X.J.; Yu, H.; Zhou, C. Deep neural network based predictions of protein interactions using primary sequences. Molecules, 2018, 23(8), E1923
Hashemifar, S.; Neyshabur, B.; Khan, A.A.; Xu, J. Predicting protein-protein interactions through sequence-based deep learning. Bioinformatics, 2018, 34(17), i802-i810.
[] [PMID: 30423091]
Marks, D.S.; Hopf, T.A.; Sander, C. Protein structure prediction from sequence variation. Nat. Biotechnol., 2012, 30(11), 1072-1080.
[] [PMID: 23138306]
Li, H.; Lyu, Q.; Cheng, J. A template-based protein structure reconstruction method using deep autoencoder learning. J. Proteomics Bioinform., 2016, 9(12), 306-313.
[] [PMID: 29081613]
Mabrouk, M.; Werner, T.; Schneider, M.; Putz, I.; Brock, O. Analysis of free modeling predictions by RBO aleph in CASP11. Proteins, 2016, 84(Suppl. 1), 87-104.
[] [PMID: 26492194]
Michel, M.; Menéndez Hurtado, D.; Uziela, K.; Elofsson, A. Large-scale structure prediction by improved contact predictions and model quality assessment. Bioinformatics, 2017, 33(14), i23-i29.
[] [PMID: 28881974]
Kim, D.E.; Dimaio, F.; Yu-Ruei Wang, R.; Song, Y.; Baker, D. One contact for every twelve residues allows robust and accurate topology-level protein structure modeling. Proteins, 2014, 82(Suppl. 2), 208-218.
[] [PMID: 23900763]
Eickholt, J.; Cheng, J. Predicting protein residue-residue contacts using deep networks and boosting. Bioinformatics, 2012, 28(23), 3066-3072.
[] [PMID: 23047561]
Wang, S.; Sun, S.; Li, Z.; Zhang, R.; Xu, J. Accurate de novo prediction of protein contact map by ultra-deep learning model. PLOS Comput. Biol., 2017, 13(1), e1005324
[] [PMID: 28056090]
Wang, S.; Sun, S.; Xu, J. Analysis of deep learning methods for blind protein contact prediction in CASP12. Proteins, 2018, 86(Suppl. 1), 67-77.
[] [PMID: 28845538]
Adhikari, B.; Hou, J.; Cheng, J. DNCON2: improved protein contact prediction using two-level deep convolutional neural networks. Bioinformatics, 2018, 34(9), 1466-1472.
[] [PMID: 29228185]
Du, T.; Liao, L.; Wu, C.H.; Sun, B. Prediction of residue-residue contact matrix for protein-protein interaction with Fisher score features and deep learning. Methods, 2016, 110, 97-105.
[] [PMID: 27282356]
Zeng, H.; Wang, S.; Zhou, T.; Zhao, F.; Li, X.; Wu, Q.; Xu, J. ComplexContact: a web server for inter-protein contact prediction using deep learning. Nucleic Acids Res., 2018, 46(W1), W432-W437.
[] [PMID: 29790960]
Wang, S.; Sun, S.; Li, Z.; Zhang, R.; Xu, J. Accurate de novo prediction of protein contact map by ultra-deep learning model. PLOS Comput. Biol., 2017, 13(1), e1005324
[] [PMID: 28056090]
Adhikari, B.; Hou, J.; Cheng, J. Protein contact prediction by integrating deep multiple sequence alignments, coevolution and machine learning. Proteins, 2018, 86(Suppl. 1), 84-96.
[] [PMID: 29047157]
Wang, L.; You, Z.H.; Xia, S.X.; Chen, X.; Yan, X.; Zhou, Y.; Liu, F. An improved efficient rotation forest algorithm to predict the interactions among proteins. Soft Comput., 2018, 22(10), 3373-3381.
Xing,, C.; Ren, B.; Chen, M.; Wang, Q.; Zhang, L.; Yan, G. NLLSS: predicting synergistic drug combinations based on semi-supervised learning. PLOS Comput. Biol., 2016, 12(7), e1004975
[] [PMID: 27415801]
Kim, S.; Jin, D.; Lee, H. Predicting drug-target interactions using drug-drug interactions. PLoS One, 2013, 8(11), e80129
[] [PMID: 24278248]
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
[] [PMID: 22666371]
Wang, Y.; Zeng, J. Predicting drug-target interactions using restricted Boltzmann machines. Bioinformatics, 2013, 29(13), i126-i134.
[] [PMID: 23104887]
Unterthiner, T.; Mayr, A.; Klambauer, G.; Hochreiter, S. Toxicity prediction using deep learning. arXiv:1503.01445,, 2015.
Ragoza, M.; Hochuli, J.; Idrobo, E.; Sunseri, J.; Koes, D.R. Protein-ligand scoring with convolutional neural networks. J. Chem. Inf. Model., 2017, 57(4), 942-957.
[] [PMID: 28368587]
Xie, L.; He, S.; Song, X.; Bo, X.; Zhang, Z. Deep learning-based transcriptome data classification for drug-target interaction prediction. BMC Genomics, 2018, 19(Suppl. 7), 667.
[] [PMID: 30255785]
Wang, L.; You, Z.H.; Chen, X.; Xia, S.X.; Liu, F.; Yan, X.; Zhou, Y.; Song, K.J. A Computational-based method for predicting drug-target interactions by using stacked autoencoder deep neural network. J. Comput. Biol., 2018, 25(3), 361-373.
[] [PMID: 28891684]
Wang, Q.; Feng, Y.; Huang, J.; Wang, T.; Cheng, G. A novel framework for the identification of drug target proteins: Combining stacked auto-encoders with a biased support vector machine. PLoS One, 2017, 12(4), e0176486
[] [PMID: 28453576]
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.
[] [PMID: 28264154]
Liu, S.; Shen, F.; Komandur Elayavilli, R.; Wang, Y.; Rastegar-Mojarad, M.; Chaudhary, V.; Liu, H. Extracting chemical-protein relations using attention-based neural networks. Database , 2018.
[] [PMID: 30295724]
Hamanaka, M.; Taneishi, K.; Iwata, H.; Ye, J.; Pei, J.; Hou, J.; Okuno, Y. CGBVS-DNN: Prediction of compound-protein interactions based on deep learning. Mol. Inform., 2017, 36(1-2), 1600045
[] [PMID: 27515489]
Gonczarek, A.; Tomczak, J.M.; Zaręba, S.; Kaczmar, J.; Dąbrowski, P.; Walczak, M.J. Interaction prediction in structure-based virtual screening using deep learning. Comput. Biol. Med., 2018, 100, 253-258.
[] [PMID: 28941550]
Corbett, P.; Boyle, J. Improving the learning of chemical-protein interactions from literature using transfer learning and specialized word embeddings. Database , 2018, 2018, bay066
[] [PMID: 30010749]
Pereira, J.C.; Caffarena, E.R.; Dos Santos, C.N. Boosting docking-based virtual screening with deep learning. J. Chem. Inf. Model., 2016, 56(12), 2495-2506.
[] [PMID: 28024405]
Stepniewska-Dziubinska, M.M.; Zielenkiewicz, P.; Siedlecki, P. Development and evaluation of a deep learning model for protein-ligand binding affinity prediction. Bioinformatics, 2018, 34(21), 3666-3674.
[] [PMID: 29757353]
Chen, X.; Huang, L.; Xie, D.; Zhao, Q. EGBMMDA: Extreme gradient boosting machine for MiRNA-disease association prediction. Cell Death Dis., 2018, 9(1), 3.
[] [PMID: 29305594]

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Year: 2020
Published on: 27 April, 2020
Page: [359 - 369]
Pages: 11
DOI: 10.2174/0929866526666190723114142
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