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Current Topics in Medicinal Chemistry

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ISSN (Print): 1568-0266
ISSN (Online): 1873-4294

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Current Frontiers

Analysis of Drug Repositioning and Prediction Techniques: A Concise Review

Author(s): Shida He, Xin Liu*, Xiucai Ye* and Sakurai Tetsuya

Volume 22, Issue 23, 2022

Published on: 21 April, 2022

Page: [1897 - 1906] Pages: 10

DOI: 10.2174/1568026622666220317164016

Price: $65

Abstract

High costs and risks are common issues in traditional drug research and development. Usually, it takes a long time to research and develop a drug, the effects of which are limited to relatively few targets. At present, studies are aiming to identify unknown new uses for existing drugs. Drug repositioning enables drugs to be quickly launched into clinical practice at a low cost because they have undergone clinical safety testing during the development process, which can greatly reduce costs and the risks of failed development. In addition to existing drugs with known indications, drugs that were shelved because of clinical trial failure can also be options for repositioning. In fact, many widely used drugs are identified via drug repositioning at present. This article reviews some popular research areas in the field of drug repositioning and briefly introduces the advantages and disadvantages of these methods, aiming to provide useful insights into future development in this field.

Keywords: Drug repositioning, Biomedicine, Drug discovery, Prediction techniques, Drug redistribution, Therapeutic conversion.

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Graphical Abstract

[1]
Drews, J. Drug discovery: A historical perspective. Science, 2000, 287(5460), 1960-1964.
[http://dx.doi.org/10.1126/science.287.5460.1960] [PMID: 10720314]
[2]
Jakhar, R.; Dangi, M.; Khichi, A.; Chhillar, A.K. Relevance of molecular docking studies in drug designing. Curr. Bioinform., 2020, 15(4), 270-278.
[http://dx.doi.org/10.2174/1574893615666191219094216]
[3]
Agarwal, S.; Agarwal, V.; Agarwal, M.; Singh, M. Exosomes: Structure, biogenesis, types and application in diagnosis and gene and drug delivery. Curr. Gene Ther., 2020, 20(3), 195-206.
[http://dx.doi.org/10.2174/1566523220999200731011702] [PMID: 32787759]
[4]
Yazdanian, M.; Briggs, K.; Jankovsky, C.; Hawi, A. The “high solubility” definition of the current FDA Guidance on Biopharmaceutical Classification System may be too strict for acidic drugs. Pharm. Res., 2004, 21(2), 293-299.
[http://dx.doi.org/10.1023/B:PHAM.0000016242.48642.71] [PMID: 15032311]
[5]
Yang, J.; Peng, S.; Zhang, B.; Houten, S.; Schadt, E.; Zhu, J.; Suh, Y.; Tu, Z. Human geroprotector discovery by targeting the converging subnetworks of aging and age-related diseases. Geroscience, 2020, 42(1), 353-372.
[http://dx.doi.org/10.1007/s11357-019-00106-x] [PMID: 31637571]
[6]
Liu, C.; Wei, D.; Xiang, J.; Ren, F.; Huang, L.; Lang, J.; Tian, G.; Li, Y.; Yang, J. An improved anticancer drug-response prediction based on an ensemble method integrating matrix completion and ridge regression. Mol. Ther. Nucleic Acids, 2020, 21, 676-686.
[http://dx.doi.org/10.1016/j.omtn.2020.07.003] [PMID: 32759058]
[7]
Zhang, S.; Su, M.; Sun, Z.; Lu, H.; Zhang, Y. The signature of pharmaceutical sensitivity based on ctDNA mutation in eleven cancers. Exp. Biol. Med. (Maywood), 2020, 245(8), 720-732.
[http://dx.doi.org/10.1177/1535370220906518] [PMID: 32050795]
[8]
Ashburn, T.T.; Thor, K.B.J.N.r.D.d. Drug repositioning: Identifying and developing new uses for existing drugs. 2004, 3(8), 673-683.
[http://dx.doi.org/10.1038/nrd1468]
[9]
Boguski, M.S.; Mandl, K.D.; Sukhatme, V.P.J.S. Repurposing with a difference. 2009, 324(5933), 1394-1395.
[http://dx.doi.org/10.1126/science.1169920]
[10]
Graul, A.I.; Cruces, E.; Stringer, M. The year’s new drugs & biologics, 2013: Part I. Drugs Today (Barc), 2014, 50(1), 51-100.
[http://dx.doi.org/10.1358/dot.2014.50.1.2116673] [PMID: 24524105]
[11]
Hurle, M.R.; Yang, L.; Xie, Q.; Rajpal, D.K.; Sanseau, P.; Agarwal, P. Computational drug repositioning: From data to therapeutics. Clin. Pharmacol. Ther., 2013, 93(4), 335-341.
[http://dx.doi.org/10.1038/clpt.2013.1] [PMID: 23443757]
[12]
Liu, K.; Chen, W. iMRM: A platform for simultaneously identifying multiple kinds of RNA modifications. Bioinformatics, 2020, 36(11), 3336-3342.
[http://dx.doi.org/10.1093/bioinformatics/btaa155] [PMID: 32134472]
[13]
Su, R.; Wu, H.; Xu, B.; Liu, X.; Wei, L. Developing a multi-dose computational model for drug-induced hepatotoxicity prediction based on toxicogenomics data. IEEE/ACM Trans. Comput. Biol. Bioinformatics, 2019, 16(4), 1231-1239.
[http://dx.doi.org/10.1109/TCBB.2018.2858756] [PMID: 30040651]
[14]
Cheng, L.; Qi, C.; Zhuang, H.; Fu, T.; Zhang, X. gutMDisorder: A comprehensive database for dysbiosis of the gut microbiota in disorders and interventions. Nucleic Acids Res., 2020, 48(D1), D554-D560.
[http://dx.doi.org/10.1093/nar/gkz843] [PMID: 31584099]
[15]
Li, T.; Huang, T.; Guo, C.; Wang, A.; Shi, X.; Mo, X.; Lu, Q.; Sun, J.; Hui, T.; Tian, G.; Wang, L.; Yang, J. Genomic variation, origin tracing, and vaccine development of SARS-CoV-2: A systematic review. Innovation (N Y), 2021, 2(2), 100116.
[http://dx.doi.org/10.1016/j.xinn.2021.100116] [PMID: 33997827]
[16]
Li, M. Identification of genes of four malignant tumors and a novel prediction model development based on PPI data and support vector machines. 2020, 27(9), 715-725.
[http://dx.doi.org/10.1038/s41417-019-0143-5]
[17]
Liu, X.; Yang, J.; Zhang, Y.; Fang, Y.; Wang, F.; Wang, J.; Zheng, X.; Yang, J. A systematic study on drug-response associated genes using baseline gene expressions of the Cancer Cell Line Encyclopedia. Sci. Rep., 2016, 6(1), 22811.
[http://dx.doi.org/10.1038/srep22811] [PMID: 26960563]
[18]
Zeng, X.; Zhu, S.; Liu, X.; Zhou, Y.; Nussinov, R.; Cheng, F. deepDR: A network-based deep learning approach to in silico drug repositioning. Bioinformatics, 2019, 35(24), 5191-5198.
[http://dx.doi.org/10.1093/bioinformatics/btz418] [PMID: 31116390]
[19]
An, Q.; Yu, L. A heterogeneous network embedding framework for predicting similarity-based drug-target interactions. Brief. Bioinform., 2021, 22(6), bbab275.
[http://dx.doi.org/10.1093/bib/bbab275] [PMID: 34373895]
[20]
Zeng, X.; Zhu, S.; Lu, W.; Liu, Z.; Huang, J.; Zhou, Y.; Fang, J.; Huang, Y.; Guo, H.; Li, L.; Trapp, B.D.; Nussinov, R.; Eng, C.; Loscalzo, J.; Cheng, F. Target identification among known drugs by deep learning from heterogeneous networks. Chem. Sci. (Camb.), 2020, 11(7), 1775-1797.
[http://dx.doi.org/10.1039/C9SC04336E] [PMID: 34123272]
[21]
Zeng, X.; Liao, Y.; Liu, Y.; Zou, Q. Prediction and validation of disease genes using HeteSim scores. IEEE/ACM Trans. Comput. Biol. Bioinformatics, 2017, 14(3), 687-695.
[http://dx.doi.org/10.1109/TCBB.2016.2520947] [PMID: 26890920]
[22]
Zeng, X. Predicting disease-associated circular RNAs using deep forests combined with positive-unlabeled learning methods. Brief. Bioinform., 2020, 21(4), 1425-1436.
[http://dx.doi.org/10.1093/bib/bbz080] [PMID: 31612203]
[23]
Yu, L. Prediction of drug response in multilayer networks based on fusion of multiomics data. Methods, 2021, 192, 85-92.
[24]
Wang, J.; Shi, Y.; Wang, X.; Chang, H. A drug target interaction prediction based on LINE-RF learning. Curr. Bioinform., 2020, 15(7), 750-757.
[http://dx.doi.org/10.2174/1574893615666191227092453]
[25]
Wang, J.; Wang, H.; Wang, X.; Chang, H. Predicting drug-target interactions via FM-DNN learning. Curr. Bioinform., 2020, 15(1), 68-76.
[http://dx.doi.org/10.2174/1574893614666190227160538]
[26]
Yu, L.S.; Shi, Y.; Zou, Q.; Wang, S.; Zheng, L.; Gao, L.Y.; Zou, Q.; Wang, S.; Zheng, L.; Gao, L. Exploring drug treatment patterns based on the action of drug and multilayer network model. Int. J. Mol. Sci., 2020, 21(14), 5014.
[http://dx.doi.org/10.3390/ijms21145014]
[27]
Mo, F.; Luo, Y.; Fan, D.; Zeng, H.; Zhao, Y.; Luo, M.; Liu, X.; Ma, X. Integrated analysis of mRNA-seq and miRNA-seq to Identify c-MYC, YAP1 and miR-3960 as major players in the anticancer effects of caffeic acid phenethyl ester in human small cell lung cancer Cell Line. Curr. Gene Ther., 2020, 20(1), 15-24.
[http://dx.doi.org/10.2174/1566523220666200523165159] [PMID: 32445454]
[28]
Liu, M.L.; Su, W.; Wang, J.S.; Yang, Y.H.; Yang, H.; Lin, H. Predicting preference of transcription factors for methylated DNA using sequence information. Mol. Ther. Nucleic Acids, 2020, 22, 1043-1050.
[http://dx.doi.org/10.1016/j.omtn.2020.07.035] [PMID: 33294291]
[29]
Cai, L. ITP-Pred: An interpretable method for predicting, therapeutic peptides with fused features low-dimension representation. Brief. Bioinform., 2021, 22(4), bbaa367.
[PMID: 33313672]
[30]
Fu, X.; Cai, L.; Zeng, X.; Zou, Q. StackCPPred: A stacking and pairwise energy content-based prediction of cell-penetrating peptides and their uptake efficiency. Bioinformatics, 2020, 36(10), 3028-3034.
[http://dx.doi.org/10.1093/bioinformatics/btaa131] [PMID: 32105326]
[31]
Yu, L.; Xia, M.; An, Q. A network embedding framework based on integrating multiplex network for drug combination prediction. Brief. Bioinform., 2021, 23(1), bbab364.
[PMID: 34505623]
[32]
Hu, Y.; Zhang, H.; Liu, B.; Gao, S.; Wang, T.; Han, Z.; Ji, X.; Liu, G. rs34331204 regulates TSPAN13 expression and contributes to Alzheimer’s disease with sex differences. Brain, 2020, 143(11), e95.
[http://dx.doi.org/10.1093/brain/awaa302] [PMID: 33175954]
[33]
Hu, Y.; Sun, J.Y.; Zhang, Y.; Zhang, H.; Gao, S.; Wang, T.; Han, Z.; Wang, L.; Sun, B.L.; Liu, G. rs1990622 variant associates with Alzheimer’s disease and regulates TMEM106B expression in human brain tissues. BMC Med., 2021, 19(1), 11.
[http://dx.doi.org/10.1186/s12916-020-01883-5] [PMID: 33461566]
[34]
Hu, Y.; Qiu, S.; Cheng, L. Integration of multiple-Omics data to analyze the population-specific differences for coronary artery disease. Comput. Math. Methods Med., 2021, 2021, 7036592.
[http://dx.doi.org/10.1155/2021/7036592] [PMID: 34447459]
[35]
Gottlieb, A.; Stein, G.Y.; Ruppin, E.; Sharan, R. PREDICT: A method for inferring novel drug indications with application to personalized medicine. Mol. Syst. Biol., 2011, 7(1), 496.
[http://dx.doi.org/10.1038/msb.2011.26] [PMID: 21654673]
[36]
Yang, L.; Agarwal, P. Systematic drug repositioning based on clinical side-effects. PLoS One, 2011, 6(12), e28025.
[http://dx.doi.org/10.1371/journal.pone.0028025] [PMID: 22205936]
[37]
Wang, Y.; Chen, S.; Deng, N.; Wang, Y. Drug repositioning by kernel-based integration of molecular structure, molecular activity, and phenotype data. PLoS One, 2013, 8(11), e78518.
[http://dx.doi.org/10.1371/journal.pone.0078518] [PMID: 24244318]
[38]
Wang, W.; Yang, S.; Zhang, X.; Li, J. Drug repositioning by integrating target information through a heterogeneous network model. Bioinformatics, 2014, 30(20), 2923-2930.
[http://dx.doi.org/10.1093/bioinformatics/btu403] [PMID: 24974205]
[39]
Martinez, V. DrugNet: Network-based drug-disease prioritization by integrating heterogeneous data. 2015, 63(1), 41-49.
[http://dx.doi.org/10.1016/j.artmed.2014.11.003]
[40]
Sirota, M.; Dudley, J.T.; Kim, J.; Chiang, A.P.; Morgan, A.A.; Sweet-Cordero, A.; Sage, J.; Butte, A.J. Discovery and preclinical validation of drug indications using compendia of public gene expression data. Sci. Transl. Med., 2011, 3(96), 96ra77.
[http://dx.doi.org/10.1126/scitranslmed.3001318] [PMID: 21849665]
[41]
Cheng, L.; Yang, H.; Zhao, H.; Pei, X.; Shi, H.; Sun, J.; Zhang, Y.; Wang, Z.; Zhou, M. MetSigDis: A manually curated resource for the metabolic signatures of diseases. Brief. Bioinform., 2019, 20(1), 203-209.
[http://dx.doi.org/10.1093/bib/bbx103] [PMID: 28968812]
[42]
Mirza, N.; Sills, G.J.; Pirmohamed, M.; Marson, A.G. Identifying new antiepileptic drugs through genomics-based drug repurposing. Hum. Mol. Genet., 2017, 26(3), 527-537.
[http://dx.doi.org/10.1093/hmg/ddw410] [PMID: 28053048]
[43]
Keiser, M.J.; Setola, V.; Irwin, J.J.; Laggner, C.; Abbas, A.I.; Hufeisen, S.J.; Jensen, N.H.; Kuijer, M.B.; Matos, R.C.; Tran, T.B.; Whaley, R.; Glennon, R.A.; Hert, J.; Thomas, K.L.; Edwards, D.D.; Shoichet, B.K.; Roth, B.L. Predicting new molecular targets for known drugs. Nature, 2009, 462(7270), 175-181.
[http://dx.doi.org/10.1038/nature08506] [PMID: 19881490]
[44]
Chen, L. A hybrid method for prediction and repositioning of drug anatomical therapeutic chemical classes., 2014, 10(4), 868, 877.
[http://dx.doi.org/10.1039/c3mb70490d]
[45]
Campillos, M.; Kuhn, M.; Gavin, A.C.; Jensen, L.J.; Bork, P. Drug target identification using side-effect similarity. Science, 2008, 321(5886), 263-266.
[http://dx.doi.org/10.1126/science.1158140] [PMID: 18621671]
[46]
Zulfiqar, H.; Masoud, M.S.; Yang, H.; Han, S-G.; Wu, C-Y.; Lin, H. Screening of prospective plant compounds as H1R and CL1R inhibitors and its antiallergic efficacy through molecular docking approach. Comput. Math. Methods Med., 2021, 2021, 6683407.
[http://dx.doi.org/10.1155/2021/6683407]
[47]
Dakshanamurthy, S.; Issa, N.T.; Assefnia, S.; Seshasayee, A.; Peters, O.J.; Madhavan, S.; Uren, A.; Brown, M.L.; Byers, S.W. Predicting new indications for approved drugs using a proteochemometric method. J. Med. Chem., 2012, 55(15), 6832-6848.
[http://dx.doi.org/10.1021/jm300576q] [PMID: 22780961]
[48]
Cooke, R.M.; Brown, A.J.; Marshall, F.H.; Mason, J.S. Structures of G protein-coupled receptors reveal new opportunities for drug discovery. Drug Discov. Today, 2015, 20(11), 1355-1364.
[http://dx.doi.org/10.1016/j.drudis.2015.08.003] [PMID: 26303408]
[49]
Pagadala, N.S.; Syed, K.; Tuszynski, J. Software for molecular docking: A review. Biophys. Rev., 2017, 9(2), 91-102.
[http://dx.doi.org/10.1007/s12551-016-0247-1] [PMID: 28510083]
[50]
Zhang, T.; Hu, Y.; Wu, X.; Ma, R.; Jiang, Q.; Wang, Y. Identifying liver cancer-related enhancer SNPs by integrating GWAS and histone Modification ChIP-seq Data. BioMed Res. Int., 2016, 2016, 2395341.
[http://dx.doi.org/10.1155/2016/2395341] [PMID: 27429976]
[51]
Grover, M.P.; Ballouz, S.; Mohanasundaram, K.A.; George, R.A.; Goscinski, A.; Crowley, T.M.; Sherman, C.D.; Wouters, M.A. Novel therapeutics for coronary artery disease from genome-wide association study data. BMC Med. Genomics, 2015, 8(S2)(Suppl. 2), S1.
[http://dx.doi.org/10.1186/1755-8794-8-S2-S1] [PMID: 26044129]
[52]
Yang, J.; Li, Z.; Fan, X.; Cheng, Y. Drug-disease association and drug-repositioning predictions in complex diseases using causal inference-probabilistic matrix factorization. J. Chem. Inf. Model., 2014, 54(9), 2562-2569.
[http://dx.doi.org/10.1021/ci500340n] [PMID: 25116798]
[53]
Piñero, J.; Bravo, À.; Queralt-Rosinach, N.; Gutiérrez-Sacristán, A.; Deu-Pons, J.; Centeno, E.; García-García, J.; Sanz, F.; Furlong, L.I. DisGeNET: a comprehensive platform integrating information on human disease-associated genes and variants. Nucleic Acids Res., 2017, 45(D1), D833-D839.
[54]
Piñero, J.; Queralt-Rosinach, N.; Bravo, À.; Deu-Pons, J.; Bauer-Mehren, A.; Baron, M.; Sanz, F.; Furlong, L.I. DisGeNET: A discovery platform for the dynamical exploration of human diseases and their genes. Database, 2015, 2015, bav028.
[http://dx.doi.org/10.1093/database/bav028]
[55]
Piñero, J.; Bravo, À.; Queralt-Rosinach, N.; Gutiérrez-Sacristán, A.; Deu-Pons, J.; Centeno, E.; García-García, J.; Sanz, F.; Furlong, L.I. The DisGeNET knowledge platform for disease genomics: 2019 update. Nucleic Acids Res., 2020, 48(D1), D845-D855.
[56]
Piñero, J.; Sauch, J.; Sanz, F.; Furlong, L.I. The DisGeNET cytoscape app: Exploring and visualiz-ing disease genomics data. Comput. Struct. Biotechnol. J., 2021, 19, 2960-2967.
[57]
Rastegar-Mojarad, M. A new method for prioritizing drug repositioning candidates extracted by literature-based discovery. IEEE International Conference on Bioinformatics and Biomedicine - Medical Informatics and Decision Making, Washington, DC. 2015.
[http://dx.doi.org/10.1109/BIBM.2015.7359766]
[58]
Greene, C.S. Voight, Greene, C.S.; Voight, B.F. Pathway and network-based strategies to translate genetic discoveries into effective therapies. Hum. Mol. Genet., 2016, 25(R2), R98-R98.
[http://dx.doi.org/10.1093/hmg/ddw160]
[59]
Iwata, M.; Hirose, L.; Kohara, H.; Liao, J.; Sawada, R.; Akiyoshi, S.; Tani, K.; Yamanishi, Y. Pathway-based drug repositioning for cancers: Computational prediction and experimental validation. J. Med. Chem., 2018, 61(21), 9583-9595.
[http://dx.doi.org/10.1021/acs.jmedchem.8b01044] [PMID: 30371064]
[60]
Greene, C.S.; Krishnan, A.; Wong, A.K.; Ricciotti, E.; Zelaya, R.A.; Himmelstein, D.S.; Zhang, R.; Hartmann, B.M.; Zaslavsky, E.; Sealfon, S.C.; Chasman, D.I.; FitzGerald, G.A.; Dolinski, K.; Grosser, T.; Troyanskaya, O.G. Understanding multicellular function and disease with human tissue-specific networks. Nat. Genet., 2015, 47(6), 569-576.
[http://dx.doi.org/10.1038/ng.3259] [PMID: 25915600]
[61]
Ding, Y.T. Jijun; Guo; Fei, Identification of drug-target interactions via dual laplacian regularized least squares with multiple kernel fusion. Knowl. Base. Syst., 2020, 204.
[62]
Ding, Y.; Tang, J.; Guo, F. Identification of drug-target interactions via fuzzy bipartite local model. Neural Comput. Appl., 2020, 23(14), 10303-10319.
[http://dx.doi.org/10.1007/s00521-019-04569-z]
[63]
Ding, Y.; Tang, J.; Guo, F. Identification of drug-target interactions via multiple information integration. Inf. Sci., 2017, 418, 546-560.
[http://dx.doi.org/10.1016/j.ins.2017.08.045]
[64]
Cheng, L.; Hu, Y.; Sun, J.; Zhou, M.; Jiang, Q. DincRNA: A comprehensive web-based bioinformatics toolkit for exploring disease associations and ncRNA function. Bioinformatics, 2018, 34(11), 1953-1956.
[http://dx.doi.org/10.1093/bioinformatics/bty002] [PMID: 29365045]
[65]
Zeng, X.; Song, X.; Ma, T.; Pan, X.; Zhou, Y.; Hou, Y.; Zhang, Z.; Li, K.; Karypis, G.; Cheng, F. Repurpose open data to discover therapeutics for COVID-19 using deep learning. J. Proteome Res., 2020, 19(11), 4624-4636.
[http://dx.doi.org/10.1021/acs.jproteome.0c00316] [PMID: 32654489]
[66]
Wu, Y.; Lu, X.; Shen, B.; Zeng, Y. The therapeutic potential and role of miRNA, lncRNA, and circRNA in osteoarthritis. Curr. Gene Ther., 2019, 19(4), 255-263.
[http://dx.doi.org/10.2174/1566523219666190716092203] [PMID: 31333128]
[67]
Cai, L.; Lu, C.; Xu, J.; Meng, Y.; Wang, P.; Fu, X.; Zeng, X.; Su, Y. Drug repositioning based on the heterogeneous information fusion graph convolutional network. Brief. Bioinform., 2021, 22(6), bbab319.
[http://dx.doi.org/10.1093/bib/bbab319] [PMID: 34378011]
[68]
Song, B.; Li, F.; Liu, Y.; Zeng, X. Deep learning methods for biomedical named entity recognition: A survey and qualitative comparison. Brief. Bioinform., 2021, 22(6), bbab282.
[http://dx.doi.org/10.1093/bib/bbab282] [PMID: 34308472]
[69]
Oh, M.; Ahn, J.; Yoon, Y. A network-based classification model for deriving novel drug-disease associations and assessing their molecular actions. PLoS One, 2014, 9(10), e111668.
[http://dx.doi.org/10.1371/journal.pone.0111668] [PMID: 25356910]
[70]
Richardson, P. Baricitinib as potential treatment for 2019-nCoV acute respiratory disease. 2020, 395(10223), 30.
[71]
Stebbing, J. Mechanism of baricitinib supports artificial intelligence‐predicted testing in COVID‐19 patients., 2020, 12(8), 12697.
[http://dx.doi.org/10.15252/emmm.202012697]
[72]
Marconi, V.C. Efficacy and safety of baricitinib for the treatment of hospitalised adults with COVID-19 (COV-BARRIER): A randomised, double-blind, parallel-group, placebo-controlled phase 3 trial. 2021, 9(12), 1407-1418.
[73]
Wang, D.; Zhang, Z.; Jiang, Y.; Mao, Z.; Wang, D.; Lin, H.; Xu, D. DM3Loc: Multi-label mRNA subcellular localization prediction and analysis based on multi-head self-attention mechanism. Nucleic Acids Res., 2021, 49(8), e46.
[http://dx.doi.org/10.1093/nar/gkab016] [PMID: 33503258]
[74]
Dao, F.Y.; Lv, H.; Zhang, D.; Zhang, Z.M.; Liu, L.; Lin, H. DeepYY1: A deep learning approach to identify YY1-mediated chromatin loops. Brief. Bioinform., 2021, 22(4), bbaa356.
[http://dx.doi.org/10.1093/bib/bbaa356] [PMID: 33279983]
[75]
Liu, Y. A deep learning approach for filtering structural variants in short read sequencing data. Brief. Bioinform., 2021, 22(4), bbaa370.
[PMID: 33378767]
[76]
Ying, W.; Zhang, L.; Deng, H. Sichuan dialect speech recognition with deep LSTM network. Front. Comput. Sci., 2020, 14(2), 378-387.
[http://dx.doi.org/10.1007/s11704-018-8030-z]
[77]
Zhang, Y.; Yan, J.; Chen, S.; Gong, M.; Gao, D.; Zhu, M.; Gan, W. Review of the applications of deep learning in bioinformatics. Curr. Bioinform., 2020, 15(8), 898-911.
[http://dx.doi.org/10.2174/1574893615999200711165743]
[78]
Cui, F.; Zhang, Z.; Zou, Q. Sequence representation approaches for sequence-based protein prediction tasks that use deep learning. Brief. Funct. Genomics, 2021, 20(1), 61-73.
[http://dx.doi.org/10.1093/bfgp/elaa030] [PMID: 33527980]
[79]
Zhang, T.; Wei, X.; Li, Z.; Shi, F.; Xia, Z.; Lian, M.; Chen, L.; Zhang, H. Natural scene nutrition information acquisition and analysis based on deep learning. Curr. Bioinform., 2020, 15(7), 662-670.
[http://dx.doi.org/10.2174/1574893614666190723121610]
[80]
Long, H.; Sun, Z.; Li, M.; Fu, H.Y.; Lin, M.C. Predicting protein phosphorylation sites based on deep learning. Curr. Bioinform., 2020, 15(4), 300-308.
[http://dx.doi.org/10.2174/1574893614666190902154332]
[81]
Lv, Z.; Ao, C.; Zou, Q. Protein function prediction: From traditional classifier to deep learning. Proteomics, 2019, 19(14), e1900119.
[http://dx.doi.org/10.1002/pmic.201900119] [PMID: 31187588]
[82]
Townes, F.W.; Irizarry, R.A. Quantile normalization of single-cell RNA-seq read counts without unique molecular identifiers. Genome Biol., 2020, 21(1), 160.
[http://dx.doi.org/10.1186/s13059-020-02078-0] [PMID: 32620142]
[83]
Tang, W.; Bertaux, F.; Thomas, P.; Stefanelli, C.; Saint, M.; Marguerat, S.; Shahrezaei, V. bayNorm: Bayesian gene expression recovery, imputation and normalization for single-cell RNA-sequencing data. Bioinformatics, 2020, 36(4), 1174-1181.
[PMID: 31584606]
[84]
Jin, Q.; Meng, Z.; Pham, T.D.; Chen, Q.; Wei, L.; Su, R. DUNet: A deformable network for retinal vessel segmentation. Knowl. Base. Syst., 2019, 178, 149-162.
[http://dx.doi.org/10.1016/j.knosys.2019.04.025]
[85]
Manavalan, B.; Basith, S.; Shin, T.H.; Wei, L.; Lee, G. Meta-4mCpred: A sequence-based meta-predictor for accurate DNA 4mC site prediction using effective feature representation. Mol. Ther. Nucleic Acids, 2019, 16, 733-744.
[http://dx.doi.org/10.1016/j.omtn.2019.04.019] [PMID: 31146255]
[86]
Su, R.; Hu, J.; Zou, Q.; Manavalan, B.; Wei, L. Empirical comparison and analysis of web-based cell-penetrating peptide prediction tools. Brief. Bioinform., 2020, 21(2), 408-420.
[http://dx.doi.org/10.1093/bib/bby124] [PMID: 30649170]
[87]
Su, R.; Liu, X.; Wei, L.; Zou, Q. Deep-Resp-Forest: A deep forest model to predict anti-cancer drug response. Methods, 2019, 166, 91-102.
[http://dx.doi.org/10.1016/j.ymeth.2019.02.009] [PMID: 30772464]
[88]
Wei, L.; Liao, M.; Gao, Y.; Ji, R.; He, Z.; Zou, Q. Improved and promising identification of human microRNAs by incorporating a high-quality negative set. IEEE/ACM Trans. Comput. Biol. Bioinformatics, 2014, 11(1), 192-201.
[http://dx.doi.org/10.1109/TCBB.2013.146] [PMID: 26355518]
[89]
Wei, L.; Wan, S.; Guo, J.; Wong, K.K. A novel hierarchical selective ensemble classifier with bioinformatics application. Artif. Intell. Med., 2017, 83, 82-90.
[http://dx.doi.org/10.1016/j.artmed.2017.02.005] [PMID: 28245947]
[90]
Wei, L.; Xing, P.; Zeng, J.; Chen, J.; Su, R.; Guo, F. Improved prediction of protein-protein interactions using novel negative samples, features, and an ensemble classifier. Artif. Intell. Med., 2017, 83, 67-74.
[http://dx.doi.org/10.1016/j.artmed.2017.03.001] [PMID: 28320624]
[91]
Jin, Q.; Cui, H.; Sun, C.; Meng, Z.; Su, R. Free-form tumor synthesis in computed tomography images via richer generative adversarial network. Knowl. Base. Syst., 2021, 218, 106753.
[http://dx.doi.org/10.1016/j.knosys.2021.106753]
[92]
Liu, J. Classification and gene selection of triple-negative breast cancer subtype embedding gene connectivity matrix in deep neural network. Brief. Bioinform., 2021, 1477-4054.
[http://dx.doi.org/10.1093/bib/bbaa395]
[93]
Su, R.; Liu, X.; Jin, Q.; Liu, X.; Wei, L. Identification of glioblastoma molecular subtype and prognosis based on deep MRI features. Knowl. Base. Syst., 2021, 232, 107490.
[http://dx.doi.org/10.1016/j.knosys.2021.107490]
[94]
Cheng, L. gutMGene: A comprehensive database for target genes of gut microbes and microbial metabolites. Nucleic Acids Res., 2022, 50(D1), D795-D800.
[PMID: 34500458]
[95]
Zhao, T.; Hu, Y.; Peng, J.; Cheng, L. DeepLGP: A novel deep learning method for prioritizing lncRNA target genes. Bioinformatics, 2020, 36(16), 4466-4472.
[http://dx.doi.org/10.1093/bioinformatics/btaa428] [PMID: 32467970]
[96]
Zhang, L.; Xiao, X.; Xu, Z.C. iPromoter-5mC: A novel fusion decision predictor for the identification of 5-methylcytosine sites in genome-wide DNA promoters. Front. Cell Dev. Biol., 2020, 8, 614.
[http://dx.doi.org/10.3389/fcell.2020.00614] [PMID: 32850787]
[97]
Xu, Z. DLpTCR: An ensemble deep learning framework for predicting immunogenic peptide recognized by T cell receptor. Brief. Bioinform., 2021, 22(6), bbab335.
[http://dx.doi.org/10.1093/bib/bbab335]
[98]
Aliper, A. Deep learning applications for predicting pharmacological properties of drugs and drug repurposing using transcriptomic data. Mol. Pharm., 2016, 13(7), 2524-2530.
[http://dx.doi.org/10.1021/acs.molpharmaceut.6b00248]
[99]
Zhao, K.; So, H-C. Drug repositioning for schizophrenia and depression/anxiety disorders: A machine learning approach leveraging expression data. IEEE J. Biomed. Health Inform., 2018, 99, 1-1.
[100]
Shang, Y.; Gao, L.; Zou, Q.; Yu, L. Prediction of drug-target interactions based on multi-layer network representation learning. Neurocomputing, 2021, 434, 80-89.
[http://dx.doi.org/10.1016/j.neucom.2020.12.068]
[101]
Liu, K.; Sun, X.; Jia, L.; Ma, J.; Xing, H.; Wu, J.; Gao, H.; Sun, Y.; Boulnois, F.; Fan, J. Chemi-Net: A molecular graph convolutional network for accurate drug property prediction. Int. J. Mol. Sci., 2019, 20(14), E3389.
[http://dx.doi.org/10.3390/ijms20143389] [PMID: 31295892]
[102]
Min, S.; B., Lee; S., Yoon Deep learning in bioinformatics. Briefings in Bioinformatics , 2017; 18, pp. (5)851-869.
[103]
Yu, L.; Wang, M.; Yang, Y.; Xu, F.; Zhang, X.; Xie, F.; Gao, L.; Li, X. Predicting therapeutic drugs for hepatocellular carcinoma based on tissue-specific pathways. PLOS Comput. Biol., 2021, 17(2), e1008696.
[http://dx.doi.org/10.1371/journal.pcbi.1008696] [PMID: 33561121]
[104]
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]
[105]
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]
[106]
Himmelstein, D.S.; Lizee, A.; Hessler, C.; Brueggeman, L.; Chen, S.L.; Hadley, D.; Green, A.; Khankhanian, P.; Baranzini, S.E. Systematic integration of biomedical knowledge prioritizes drugs for repurposing. eLife, 2017, 6, 6.
[http://dx.doi.org/10.7554/eLife.26726] [PMID: 28936969]
[107]
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]

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