Drug Repositioning Based on Deep Sparse Autoencoder and Drug-Disease Similarity.
COVID-19
Deep sparse autoencoder
Drug and disease similarity
Drug repositioning
Journal
Interdisciplinary sciences, computational life sciences
ISSN: 1867-1462
Titre abrégé: Interdiscip Sci
Pays: Germany
ID NLM: 101515919
Informations de publication
Date de publication:
16 Dec 2023
16 Dec 2023
Historique:
received:
19
07
2023
accepted:
06
11
2023
revised:
03
11
2023
medline:
16
12
2023
pubmed:
16
12
2023
entrez:
16
12
2023
Statut:
aheadofprint
Résumé
Drug repositioning is critical to drug development. Previous drug repositioning methods mainly constructed drug-disease heterogeneous networks to extract drug-disease features. However, these methods faced difficulty when we are using structurally simple models to deal with complex heterogeneous networks. Therefore, in this study, the researchers introduced a drug repositioning method named DRDSA. The method utilizes a deep sparse autoencoder and integrates drug-disease similarities. First, the researchers constructed a drug-disease feature network by incorporating information from drug chemical structure, disease semantic data, and existing known drug-disease associations. Then, we learned the low-dimensional representation of the feature network using a deep sparse autoencoder. Finally, we utilized a deep neural network to make predictions on new drug-disease associations based on the feature representation. The experimental results show that our proposed method has achieved optimal results on all four benchmark datasets, especially on the CTD dataset where AUC and AUPR reached 0.9619 and 0.9676, respectively, outperforming other baseline methods. In the case study, the researchers predicted the top ten antiviral drugs for COVID-19. Remarkably, six out of these predictions were subsequently validated by other literature sources. Schematic diagrams of data processing and DRDSA model. A Construction of drug and disease feature vectors, B The workflow of DRDSA model.
Identifiants
pubmed: 38103130
doi: 10.1007/s12539-023-00593-9
pii: 10.1007/s12539-023-00593-9
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : the National Natural Science Foundation of China
ID : 62272288
Organisme : the National Natural Science Foundation of China
ID : 61972451
Organisme : the Shenzhen Science and Technology Program
ID : KQTD20200820113106007
Organisme : Shaanxi Normal University
ID : GK202302006
Organisme : Natural Science Foundation of Hunan Province
ID : 2023JJ30411
Informations de copyright
© 2023. International Association of Scientists in the Interdisciplinary Areas.
Références
Collins FS (2016) Seeking a cure for one of the rarest diseases: progeria. Circulation 134:126–129. https://doi.org/10.1161/CIRCULATIONAHA.116.022965
doi: 10.1161/CIRCULATIONAHA.116.022965
pubmed: 27400897
pmcid: 5101939
Hurle MR, Yang L, Xie Q et al (2013) Computational drug repositioning: from data to therapeutics. Clin Pharmacol Ther 93:335–341. https://doi.org/10.1038/clpt.2013.1
doi: 10.1038/clpt.2013.1
pubmed: 23443757
Ashburn TT, Thor KB (2004) Drug repositioning: identifying and developing new uses for existing drugs. Nat Rev Drug Discov 3:673–683. https://doi.org/10.1038/nrd1468
doi: 10.1038/nrd1468
pubmed: 15286734
Lam W, Zhong N, Tan W (2003) Overview on SARS in Asia and the World. Respirology. https://doi.org/10.1046/j.1440-1843.2003.00516.x
doi: 10.1046/j.1440-1843.2003.00516.x
pubmed: 15018134
pmcid: 7159403
Shi Y, Wang G, Cai X et al (2020) An overview of COVID-19. J Zhejiang Univ Sci B 21:343–360. https://doi.org/10.1631/jzus.B2000083
doi: 10.1631/jzus.B2000083
pubmed: 32425000
pmcid: 7205601
DiMasi JA, Hansen RW, Grabowski HG (2003) The price of innovation: new estimates of drug development costs. J Health Econ 22:151–185. https://doi.org/10.1016/S0167-6296(02)00126-1
doi: 10.1016/S0167-6296(02)00126-1
pubmed: 12606142
Dudley JT, Deshpande T, Butte AJ (2011) Exploiting drug-disease relationships for computational drug repositioning. Brief Bioinform 12:303–311. https://doi.org/10.1093/bib/bbr013
doi: 10.1093/bib/bbr013
pubmed: 21690101
pmcid: 3137933
Zou J, Zheng M-W, Li G, Su Z-G (2013) Advanced systems biology methods in drug discovery and translational biomedicine. Biomed Res Int 2013:1–8. https://doi.org/10.1155/2013/742835
doi: 10.1155/2013/742835
Ye H, Liu Q, Wei J (2014) Construction of drug network based on side effects and its application for drug repositioning. PLoS One 9:e87864. https://doi.org/10.1371/journal.pone.0087864
doi: 10.1371/journal.pone.0087864
pubmed: 24505324
pmcid: 3913703
Dotolo S, Marabotti A, Facchiano A, Tagliaferri R (2021) A review on drug repurposing applicable to COVID-19. Brief Bioinform 22:726–741. https://doi.org/10.1093/bib/bbaa288
doi: 10.1093/bib/bbaa288
pubmed: 33147623
Xuan P, Cui H, Shen T et al (2019) HeteroDualNet: a dual convolutional neural network with heterogeneous layers for drug-disease association prediction via Chou’s five-step rule. Front Pharmacol 10:1301. https://doi.org/10.3389/fphar.2019.01301
doi: 10.3389/fphar.2019.01301
pubmed: 31780934
pmcid: 6856670
Jiang H-J, Huang Y-A, You Z-H (2019) Predicting drug-disease associations via using Gaussian interaction profile and kernel-based autoencoder. Biomed Res Int 2019:1–11. https://doi.org/10.1155/2019/2426958
doi: 10.1155/2019/2426958
Wang Y, Deng G, Zeng N et al (2019) Drug-disease association prediction based on neighborhood information aggregation in neural networks. IEEE Access 7:50581–50587. https://doi.org/10.1109/ACCESS.2019.2907522
doi: 10.1109/ACCESS.2019.2907522
Gottlieb A, Stein GY, Ruppin E, Sharan R (2011) PREDICT: a method for inferring novel drug indications with application to personalized medicine. Mol Syst Biol 7:496. https://doi.org/10.1038/msb.2011.26
doi: 10.1038/msb.2011.26
pubmed: 21654673
pmcid: 3159979
Perlman L, Gottlieb A, Atias N et al (2011) Combining drug and gene similarity measures for drug-target elucidation. J Comput Biol 18:133–145. https://doi.org/10.1089/cmb.2010.0213
doi: 10.1089/cmb.2010.0213
pubmed: 21314453
Zeng X, Zhu S, Liu X et al (2019) deepDR: a network-based deep learning approach to in silico drug repositioning. Bioinformatics 35:5191–5198. https://doi.org/10.1093/bioinformatics/btz418
doi: 10.1093/bioinformatics/btz418
pubmed: 31116390
pmcid: 6954645
Yu Z, Huang F, Zhao X et al (2021) Predicting drug–disease associations through layer attention graph convolutional network. Brief Bioinform 22:bbaa243. https://doi.org/10.1093/bib/bbaa243
doi: 10.1093/bib/bbaa243
pubmed: 33078832
Su X, Hu L, You Z et al (2022) A deep learning method for repurposing antiviral drugs against new viruses via multi-view nonnegative matrix factorization and its application to SARS-CoV-2. Brief Bioinform 23:bbab526. https://doi.org/10.1093/bib/bbab526
doi: 10.1093/bib/bbab526
pubmed: 34965582
Zhao B-W, You Z-H, Hu L et al (2021) A multi-graph deep learning model for predicting drug-disease associations. In: Huang D-S, Jo K-H, Li J et al (eds) Intelligent computing theories and application. Springer International Publishing, Cham, pp 580–590
doi: 10.1007/978-3-030-84532-2_52
Barabási A-L, Gulbahce N, Loscalzo J (2011) Network medicine: a network-based approach to human disease. Nat Rev Genet 12:56–68. https://doi.org/10.1038/nrg2918
doi: 10.1038/nrg2918
pubmed: 21164525
pmcid: 3140052
Ata SK, Wu M, Fang Y et al (2021) Recent advances in network-based methods for disease gene prediction. Brief Bioinform 22:bbaa303. https://doi.org/10.1093/bib/bbaa303
doi: 10.1093/bib/bbaa303
pubmed: 33276376
Kim Y, Park J-H, Cho Y-R (2022) Network-based approaches for disease-gene association prediction using protein-protein interaction networks. IJMS 23:7411. https://doi.org/10.3390/ijms23137411
doi: 10.3390/ijms23137411
pubmed: 35806415
pmcid: 9266751
Martínez V, Navarro C, Cano C et al (2015) DrugNet: network-based drug–disease prioritization by integrating heterogeneous data. Artif Intell Med 63:41–49. https://doi.org/10.1016/j.artmed.2014.11.003
doi: 10.1016/j.artmed.2014.11.003
pubmed: 25704113
Wang W, Yang S, Zhang X, Li J (2014) Drug repositioning by integrating target information through a heterogeneous network model. Bioinformatics 30:2923–2930. https://doi.org/10.1093/bioinformatics/btu403
doi: 10.1093/bioinformatics/btu403
pubmed: 24974205
pmcid: 4184255
Luo H, Wang J, Li M et al (2016) Drug repositioning based on comprehensive similarity measures and Bi-Random walk algorithm. Bioinformatics 32:2664–2671. https://doi.org/10.1093/bioinformatics/btw228
doi: 10.1093/bioinformatics/btw228
pubmed: 27153662
Wu C, Gudivada RC, Aronow BJ, Jegga AG (2013) Computational drug repositioning through heterogeneous network clustering. BMC Syst Biol 7:S6. https://doi.org/10.1186/1752-0509-7-S5-S6
doi: 10.1186/1752-0509-7-S5-S6
pubmed: 24565104
pmcid: 4029299
Wang F, Ding Y, Lei X et al (2020) Identifying gene signatures for cancer drug repositioning based on sample clustering. IEEE/ACM Trans Comput Biol Bioinf. https://doi.org/10.1109/TCBB.2020.3019781
doi: 10.1109/TCBB.2020.3019781
March-Vila E, Pinzi L, Sturm N et al (2017) On the integration of in silico drug design methods for drug repurposing. Front Pharmacol 8:298. https://doi.org/10.3389/fphar.2017.00298
doi: 10.3389/fphar.2017.00298
pubmed: 28588497
pmcid: 5440551
Zhang W, Chen Y, Li D, Yue X (2018) Manifold regularized matrix factorization for drug-drug interaction prediction. J Biomed Inform 88:90–97. https://doi.org/10.1016/j.jbi.2018.11.005
doi: 10.1016/j.jbi.2018.11.005
pubmed: 30445219
Zhang W, Jing K, Huang F et al (2019) SFLLN: a sparse feature learning ensemble method with linear neighborhood regularization for predicting drug–drug interactions. Inf Sci 497:189–201. https://doi.org/10.1016/j.ins.2019.05.017
doi: 10.1016/j.ins.2019.05.017
Zhang W, Liu X, Chen Y et al (2018) Feature-derived graph regularized matrix factorization for predicting drug side effects. Neurocomputing 287:154–162. https://doi.org/10.1016/j.neucom.2018.01.085
doi: 10.1016/j.neucom.2018.01.085
Chen X, Yan CC, Zhang X et al (2016) Drug–target interaction prediction: databases, web servers and computational models. Brief Bioinform 17:696–712. https://doi.org/10.1093/bib/bbv066
doi: 10.1093/bib/bbv066
pubmed: 26283676
Zhang W, Yue X, Lin W et al (2018) Predicting drug-disease associations by using similarity constrained matrix factorization. BMC Bioinformatics 19:233. https://doi.org/10.1186/s12859-018-2220-4
doi: 10.1186/s12859-018-2220-4
pubmed: 29914348
pmcid: 6006580
Xu X, Long H, Xi B et al (2019) Molecular network-based drug prediction in thyroid cancer. IJMS 20:263. https://doi.org/10.3390/ijms20020263
doi: 10.3390/ijms20020263
pubmed: 30641858
pmcid: 6359462
Guan N-N, Zhao Y, Wang C-C et al (2019) Anticancer drug response prediction in cell lines using weighted graph regularized matrix factorization. Mol Ther Nucleic Acids 17:164–174. https://doi.org/10.1016/j.omtn.2019.05.017
doi: 10.1016/j.omtn.2019.05.017
pubmed: 31265947
pmcid: 6610642
Zhao Y, Chen X, Yin J (2018) A novel computational method for the identification of potential miRNA-disease association based on symmetric non-negative matrix factorization and Kronecker regularized least square. Front Genet 9:324. https://doi.org/10.3389/fgene.2018.00324
doi: 10.3389/fgene.2018.00324
pubmed: 30186308
pmcid: 6111239
Dai W, Liu X, Gao Y et al (2015) Matrix factorization-based prediction of novel drug indications by integrating genomic space. Comput Math Methods Med 2015:1–9. https://doi.org/10.1155/2015/275045
doi: 10.1155/2015/275045
Xuan P, Song Y, Zhang T, Jia L (2019) Prediction of potential drug-disease associations through deep integration of diversity and projections of various drug features. IJMS 20:4102. https://doi.org/10.3390/ijms20174102
doi: 10.3390/ijms20174102
pubmed: 31443472
pmcid: 6747548
Zhang Y, Lei X, Pan Y, Wu F-X (2022) Drug repositioning with GraphSAGE and clustering constraints based on drug and disease networks. Front Pharmacol 13:872785. https://doi.org/10.3389/fphar.2022.872785
doi: 10.3389/fphar.2022.872785
pubmed: 35620297
pmcid: 9127467
Liang X, Zhang P, Yan L et al (2017) LRSSL: predict and interpret drug–disease associations based on data integration using sparse subspace learning. Bioinformatics 33:1187–1196. https://doi.org/10.1093/bioinformatics/btw770
doi: 10.1093/bioinformatics/btw770
pubmed: 28096083
Meng Y, Jin M, Tang X, Xu J (2021) Drug repositioning based on similarity constrained probabilistic matrix factorization: COVID-19 as a case study. Appl Soft Comput 103:107135. https://doi.org/10.1016/j.asoc.2021.107135
doi: 10.1016/j.asoc.2021.107135
pubmed: 33519322
pmcid: 7825831
Steinbeck C, Han Y, Kuhn S et al (2003) The chemistry development kit (CDK): an open-source java library for chemo- and bioinformatics. J Chem Inf Comput Sci 43:493–500. https://doi.org/10.1021/ci025584y
doi: 10.1021/ci025584y
pubmed: 12653513
pmcid: 4901983
Weininger D (1988) SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. J Chem Inf Comput Sci 28:31–36. https://doi.org/10.1021/ci00057a005
doi: 10.1021/ci00057a005
Tanimoto TT (1958) An elementary mathematical theory of classification and prediction. https://xueshu.baidu.com/usercenter/paper/show?paperid=5aade0fa71c478ae6f297921c4ca1dd8&site=xueshu_se&hitarticle=1
Wang D, Wang J, Lu M et al (2010) Inferring the human microRNA functional similarity and functional network based on microRNA-associated diseases. Bioinformatics 26:1644–1650. https://doi.org/10.1093/bioinformatics/btq241
doi: 10.1093/bioinformatics/btq241
pubmed: 20439255
Yuan FN, Zhang L, Shi JT et al (2019) Review on theoretical and practical aspects of autoencoder neural networks. Chin J Comput (in Chinese) 42(1):28. https://xueshu.baidu.com/usercenter/paper/show?paperid=1g5c0es0ru6v00w0jr3n00j007257167&site=xueshu_se
Fu H, Huang F, Liu X et al (2022) MVGCN: data integration through multi-view graph convolutional network for predicting links in biomedical bipartite networks. Bioinformatics 38:426–434. https://doi.org/10.1093/bioinformatics/btab651
doi: 10.1093/bioinformatics/btab651
pubmed: 34499148
Yang M, Luo H, Li Y, Wang J (2019) Drug repositioning based on bounded nuclear norm regularization. Bioinformatics 35:i455–i463. https://doi.org/10.1093/bioinformatics/btz331
doi: 10.1093/bioinformatics/btz331
pubmed: 31510658
pmcid: 6612853
Zhang Z-C, Zhang X-F, Wu M et al (2020) A graph regularized generalized matrix factorization model for predicting links in biomedical bipartite networks. Bioinformatics 36:3474–3481. https://doi.org/10.1093/bioinformatics/btaa157
doi: 10.1093/bioinformatics/btaa157
pubmed: 32145009
Lu L, Qin J, Chen J et al (2022) Recent computational drug repositioning strategies against SARS-CoV-2. Comput Struct Biotechnol J 20:5713–5728. https://doi.org/10.1016/j.csbj.2022.10.017
doi: 10.1016/j.csbj.2022.10.017
pubmed: 36277237
pmcid: 9575573
Ab Ghani NS, Emrizal R, Makmur H, Firdaus-Raih M (2020) Side chain similarity comparisons for integrated drug repositioning and potential toxicity assessments in epidemic response scenarios: the case for COVID-19. Comput Struct Biotechnol J 18:2931–2944. https://doi.org/10.1016/j.csbj.2020.10.013
doi: 10.1016/j.csbj.2020.10.013
pubmed: 33101604
pmcid: 7575501
Wang Y, Zhang D, Du G et al (2020) Remdesivir in adults with severe COVID-19: a randomised, double-blind, placebo-controlled, multicentre trial. The Lancet 395:1569–1578. https://doi.org/10.1016/S0140-6736(20)31022-9
doi: 10.1016/S0140-6736(20)31022-9
Cao B, Wang Y, Wen D et al (2020) A trial of Lopinavir-Ritonavir in adults hospitalized with severe Covid-19. N Engl J Med 382:1787–1799. https://doi.org/10.1056/NEJMoa2001282
doi: 10.1056/NEJMoa2001282
pubmed: 32187464
Rizk JG, Kalantar-Zadeh K, Mehra MR et al (2020) Pharmaco-immunomodulatory therapy in COVID-19. Drugs 80:1267–1292. https://doi.org/10.1007/s40265-020-01367-z
doi: 10.1007/s40265-020-01367-z
pubmed: 32696108
pmcid: 7372203
Rajendran K, Krishnasamy N, Rangarajan J et al (2020) Convalescent plasma transfusion for the treatment of COVID-19: systematic review. J Med Virol 92:1475–1483. https://doi.org/10.1002/jmv.25961
doi: 10.1002/jmv.25961
pubmed: 32356910
pmcid: 7267113
Stone JH, Frigault MJ, Serling-Boyd NJ et al (2020) Efficacy of tocilizumab in patients hospitalized with Covid-19. N Engl J Med 383:2333–2344. https://doi.org/10.1056/NEJMoa2028836
doi: 10.1056/NEJMoa2028836
pubmed: 33085857
Chow JH, Khanna AK, Kethireddy S et al (2021) Aspirin use is associated with decreased mechanical ventilation, intensive care unit admission, and in-hospital mortality in hospitalized patients with coronavirus disease 2019. Anesth Analg 132:930–941. https://doi.org/10.1213/ANE.0000000000005292
doi: 10.1213/ANE.0000000000005292
pubmed: 33093359