Deep mining heterogeneous networks of biomedical linked data to predict novel drug-target associations

Bioinformatics

Volumn 33, Issue 15, 2017, Pages 2337-2344

  Zong, Nansu ^a   Kim, Hyeoneui ^a   Ngo, Victoria ^b   Harismendy, Olivier ^a,c  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

^c UNIVERSITY OF CALIFORNIA SAN DIEGO   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BIOLOGY; DATA MINING; HUMAN; MACHINE LEARNING; PHARMACOLOGY; PROCEDURES; SEMANTIC WEB; SOFTWARE;

COMPUTATIONAL BIOLOGY; DATA MINING; HUMANS; MACHINE LEARNING; PHARMACOLOGY; SEMANTIC WEB; SOFTWARE;

EID: 85026403314     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btx160     Document Type: Article

References (33)

1. Bass, J.I.F. et al. (2013) Using networks to measure similarity between genes: association index selection. Nat. Methods, 10, 1169-1176.
2. Belleau, F. et al. (2008) Bio2RDF: towards a mashup to build bioinformatics knowledge systems. J. Biomed. Inf., 41, 706-716.
3. Bizer, C. et al. (2009) Linked data-the story so far. International Journal on SemanticWeb and Information Systems, 5, 1-22.
4. Bleakley, K. and Yamanishi, Y. (2009) Supervised prediction of drug-target interactions using bipartite local models. Bioinformatics, 25, 2397-2403.
5. Campillos, M. et al. (2008) Drug target identification using side-effect similarity. Science, 321, 263-266.
6. Chen, B. et al. (2012a) Assessing drug target association using semantic linked data. PLoS Comput. Biol., 8, e1002574.
7. Chen, X. et al. (2012b) Drug-target interaction prediction by random walk on the heterogeneous network. Mol. BioSystems, 8, 1970-1978.
8. Cheng, A.C. et al. (2007) Structure-based maximal affinity model predicts small-molecule druggability. Nat. Biotechnol., 25, 71-75.
9. Cheng, F. et al. (2012) Prediction of drug-target interactions and drug repositioning via network-based inference. PLoS Comput. Biol., 8, e1002503.
10. Consortium, U. (2008) The universal protein resource (UniProt). Nucleic Acids Res., 36, D190-D195.
11. Ding, H. et al. (2014) Similarity-based machine learning methods for predicting drug-target interactions: a brief review. Brief. Bioinf., 15, 734-747.
12. Goh, K.-I. et al. (2007) The human disease network. Proc. Natl. Acad. Sci. U. S. A., 104, 8685-8690.
13. Hamosh, A. et al. (2005) Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders. Nucleic Acids Res., 33, D514-D517.
14. Jacob, L. and Vert, J.-P. (2008) Protein-ligand interaction prediction: an improved chemogenomics approach. Bioinformatics, 24, 2149-2156.
15. Jeh, G. and Widom, J. (2002) SimRank: a measure of structural-context similarity. In: Proceedings of the Eighth ACMSIGKDDInternational Conference on Knowledge Discovery and DataMining. ACM, pp. 538-543.
16. Mikolov, T. et al. (2013) Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781.
17. Mnih, A. and Hinton, G.E. (2008) A scalable hierarchical distributed language model. In Proceedings of the 21st International Conference on Neural Information Processing Systems (NIPS'08), Koller, D. et al. (eds). Curran Associates Inc., USA, 1081-1088.
18. Palma, G. et al. (2014) 131-146. Drug-target interaction prediction using semantic similarity and edge partitioning. In: International Semantic Web Conference. Springer.
19. Perlman, L. et al. (2011) Combining drug and gene similarity measures for drug-target elucidation. J. Comput. Biol., 18, 133-145.
20. Perozzi, B. et al. (2014) Deepwalk: Online learning of social representations. In: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM. pp. 701-710.
21. Povey, S. et al. (2001) The HUGO gene nomenclature committee (HGNC). Hum. Genet., 109, 678-680.
22. Seal, A. et al. (2015) Optimizing drug-target interaction prediction based on random walk on heterogeneous networks. J. Cheminf., 7, 1.
23. Tang, J. et al. (2015) Line: Large-scale information network embedding. In: Proceedings of the 24th International Conference on World Wide Web. ACM. pp. 1067-1077.
24. van Laarhoven, T. et al. (2011) Gaussian interaction profile kernels for predicting drug-target interaction. Bioinformatics, 27, 3036-3043.
25. Vogt, I. and Mestres, J. (2010) Drug-target networks. Mol. Inf., 29, 10-14.
26. Wang, W. et al. (2013) Drug target predictions based on heterogeneous graph inference. In: Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing. NIH Public Access. p. 53.
27. Wishart, D.S. et al. (2008) DrugBank: a knowledgebase for drugs, drug actions and drug targets. Nucleic Acids Res., 36, D901-D906.
28. Xia, Z. et al. (2010) Semi-supervised drug-protein interaction prediction from heterogeneous biological spaces. BMC Syst. Biol., 4, S6.
29. Yamanishi, Y. et al. (2008) Prediction of drug-target interaction networks from the integration of chemical and genomic spaces. Bioinformatics, 24, i232-i240.
30. Yamanishi, Y. et al. (2010) Drug-target interaction prediction from chemical, genomic and pharmacological data in an integrated framework. Bioinformatics, 26, i246-i254.
31. Yildirim, M.A. et al. (2007) Drug-target network. Nat. Biotechnol., 25, 1119-1126.
32. Yu, H. et al. (2016) Prediction of drugs having opposite effects on disease genes in a directed network. BMC Syst. Biol., 10, 17.
33. Zhu, S. et al. (2005) A probabilistic model for mining implicit 'chemical compound-gene' relations from literature. Bioinformatics, 21, ii245-ii251.