Abstract
Computational drug repositioning has been proven as a promising and efficient strategy for discovering new uses from existing drugs. To achieve this goal, a number of computational methods have been proposed, which are based on different data sources of drugs and diseases. These methods approach the problem using either machine learning- or network-based models with an assumption that similar drugs can be used for similar diseases to identify new indications of drugs. Therefore, similarities between drugs and between diseases are usually used as inputs. In addition, known drug-disease associations are also needed for the methods as prior information. It should be noted that those associations are still not well established due to the fact that many of marketed drugs have been withdrawn and this could affect the outcome of the methods. In this study, we propose a novel method named RLSDR (Regularized Least Square for Drug Repositioning) to find new uses of drugs. More specifically, it relies on a semi-supervised learning model, Regularized Least Square, thus it does not require definition of non-drug-disease associations as previously proposed machine learning-based methods. In addition, the similarity between drugs measured by chemical structures of drug compounds and the similarity between diseases which share phenotypes can be represented in a form of either similarity network or similarity matrix as inputs of the method. Moreover, instead of using a gold-standard set of known drug-disease associations, we construct an artificial set of the associations based on known disease-gene and drug-target associations. Experiment results demonstrate that RLSDR achieves better prediction performance on the artificial set of drug-disease associations than that on the gold-standard ones in terms of area under the Receiver Operating Characteristic (ROC) curve (AUC). In addition, it outperforms two representative network-based methods irrespective of the prior information of drug-disease associations. Novel indications for a number of drugs are also identified and validated by evidences from a different data resource.
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References
Amberger J, Bocchini CA, Scott AF, Hamosh A (2009) McKusick’s online mendelian inheritance in man (OMIM®). Nucleic Acids Res 37(suppl 1):D793–D796
Aronson JK (2007) Old drugs—new uses. Br J Clin Pharmacol 64(5):563–565
Ashburn TT, Thor KB (2004) Drug repositioning: identifying and developing new uses for existing drugs. Nat Rev Drug Discov 3(8):673–683
Chen X, Yan G-Y (2014) Semi-supervised learning for potential human microRNA-disease associations inference. Sci Rep 4:5501
Chen X, Yan CC, Zhang X, You Z-H, Huang Y-A, Yan G-Y (2016) HGIMDA: heterogeneous graph inference for miRNA-disease association prediction. Oncotarget 7(40):65257–65269
Dickson M, Gagnon JP (2009) The cost of new drug discovery and development. Discov Med 4(22):172–179
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
Hattori M, Tanaka N, Kanehisa M, Goto S (2010) SIMCOMP/SUBCOMP: chemical structure search servers for network analyses. Nucleic Acids Res 38(suppl 2):W652–W656
Hughes JP, Rees S, Kalindjian SB, Philpott KL (2011) Principles of early drug discovery. Br J Pharmacol 162(6):1239–1249
Hurle MR, Yang L, Xie Q, Rajpal DK, Sanseau P, Agarwal P (2013) Computational drug repositioning: from data to therapeutics. Clin Pharmacol Ther 93(4):335–341
Kanehisa M, Goto S, Furumichi M, Tanabe M, Hirakawa M (2009) KEGG for representation and analysis of molecular networks involving diseases and drugs. Nucleic Acids Res 38(suppl 1):D355–D360
Kibbe WA, Arze C, Felix V, Mitraka E, Bolton E, Fu G, Mungall CJ, Binder JX, Malone J, Vasant D, Parkinson H, Schriml LM (2015) Disease Ontology 2015 update: an expanded and updated database of human diseases for linking biomedical knowledge through disease data. Nucleic Acids Res 43(D1):D1071–D1078
Kohler S, Bauer S, Horn D, Robinson P (2008) Walking the interactome for prioritization of candidate disease genes. Am J Hum Genet 82(4):949–958
Law V, Knox C, Djoumbou Y, Jewison T, Guo AC, Liu Y, Maciejewski A, Arndt D, Wilson M, Neveu V, Tang A, Gabriel G, Ly C, Adamjee S, Dame ZT, Han B, Zhou Y, Wishart DS (2014) DrugBank 4.0: shedding new light on drug metabolism. Nucleic Acids Res 42(D1):D1091–D1097
Le D-H (2015a) Disease phenotype similarity improves the prediction of novel disease-associated microRNAs. In: Information and computer science (NICS), 2015 2nd national foundation for science and technology development conference on 16–18 Sept 2015, pp 76–81
Le D-H (2015b) Network-based ranking methods for prediction of novel disease associated microRNAs. Comput Biol Chem 58:139–148
Le D-H (2015c) A novel method for identifying disease associated protein complexes based on functional similarity protein complex networks. Algorithms Mol Biol 10(1):14
Le D-H, Dang V-T (2016) Ontology-based disease similarity network for disease gene prediction. Vietnam J Comput Sci 3:1–9
Le D-H, Kwon Y-K (2012) GPEC: a Cytoscape plug-in for random walk-based gene prioritization and biomedical evidence collection. Comput Biol Chem 37:17–23
Le D-H, Verbeke L, Son LH, Chu D-T, Pham V-H (2017) Random walks on mutual microRNA-target gene interaction network improve the prediction of disease-associated microRNAs. BMC Bioinform 18(1):479
Li J, Lu Z (2013) Pathway-based drug repositioning using causal inference. BMC Bioinform 14(16):S3
Lipscomb CE (2000) Medical subject headings (MeSH). Bull Med Libr Assoc 88(3):265–266
Liu H, Song Y, Guan J, Luo L, Zhuang Z (2016) Inferring new indications for approved drugs via random walk on drug-disease heterogenous networks. BMC Bioinform 17(17):539
Martínez V, Navarro C, Cano C, Fajardo W, Blanco A (2015) DrugNet: network-based drug–disease prioritization by integrating heterogeneous data. Artif Intell Med 63(1):41–49
Menden MP, Iorio F, Garnett M, McDermott U, Benes CH, Ballester PJ, Saez-Rodriguez J (2013) Machine learning prediction of cancer cell sensitivity to drugs based on genomic and chemical properties. PLoS ONE 8(4):e61318
Napolitano F, Zhao Y, Moreira VM, Tagliaferri R, Kere J, D’Amato M, Greco D (2013) Drug repositioning: a machine-learning approach through data integration. J Cheminform 5(1):30
Rich RM, Rosenfeld PJ, Puliafito CA, Dubovy SR, Davis JL, Flynn HWJ, Gonzalez S, Feuer WJ, Lin RC, Lalwani GA, Nguyen JK, Kumar G (2006) SHORT-term safety and efficacy of intravitreal bevacizumab (avastin) for neovascular age-related macular degeneration. Retina 26(5):495–511
Sardana D, Zhu C, Zhang M, Gudivada RC, Yang L, Jegga AG (2011) Drug repositioning for orphan diseases. Brief Bioinform 12:346–356
Sirota M, Dudley JT, Kim J, Chiang AP, Morgan AA, Sweet-Cordero A, Sage J, Butte AJ (2011) Discovery and preclinical validation of drug indications using compendia of public gene expression data. Sci Transl Med 3:96ra77
van Driel MA, Bruggeman J, Vriend G, Brunner HG, Leunissen JAM (2006) A text-mining analysis of the human phenome. Eur J Hum Genet 14(5):535–542
Wang Y, Chen S, Deng N, Wang Y (2013a) Drug repositioning by kernel-based integration of molecular structure, molecular activity, and phenotype data. PLoS ONE 8(11):e78518
Wang W, Yang S, Li J (2013b) Drug target predictions based on heterogeneous graph inference. Biocomputing 18:53–64
Wu Z, Wang Y, Chen L (2013a) Network-based drug repositioning. Mol BioSyst 9(6):1268–1281
Wu C, Gudivada RC, Aronow BJ, Jegga AG (2013b) Computational drug repositioning through heterogeneous network clustering. BMC Syst Biol 7(5):S6
Zhang P, Wang F, Hu J (2014) Towards drug repositioning: a unified computational framework for integrating multiple aspects of drug similarity and disease similarity. In: AMIA annual symposium proceedings. American Medical Informatics Association, p 1258
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Le, DH., Nguyen-Ngoc, D. Drug Repositioning by Integrating Known Disease-Gene and Drug-Target Associations in a Semi-supervised Learning Model. Acta Biotheor 66, 315–331 (2018). https://doi.org/10.1007/s10441-018-9325-z
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DOI: https://doi.org/10.1007/s10441-018-9325-z