Assessing drug target association using semantic linked data

PLoS Computational Biology

Volumn 8, Issue 7, 2012, Pages

  Chen, Bin ^a   Ding, Ying ^a   Wild, David J ^a  

^a INDIANA UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DATA MINING; DRUG INTERACTIONS; GENE EXPRESSION; LINKED DATA; WEB SERVICES;

COMPLEX MECHANISMS; DRUG DISCOVERY; DRUG TARGETS; INTEGRATED SETS; LARGE SCALE DATA; LINKED DATUM; MECHANISM OF ACTION; PUBLIC DATA; PUBLIC DATASET; SYSTEMATIC INTEGRATION;

SEMANTICS;

ICOSAPENTAENOIC ACID; LONG CHAIN FATTY ACID COENZYME A LIGASE; PEROXISOME PROLIFERATOR ACTIVATED RECEPTOR GAMMA; TROGLITAZONE;

ACCURACY; ALGORITHM; ARTICLE; BINDING AFFINITY; DATA MINING; DRUG PROTEIN BINDING; DRUG TARGETING; GENE EXPRESSION PROFILING; LIGAND BINDING; NORMAL DISTRIBUTION; PREDICTION; PROBABILITY; PROTEIN DATABASE; PROTEIN TARGETING; SEMANTIC LINK ASSOCIATION PREDICTION; STATISTICAL MODEL;

EID: 84864614370     PISSN: 1553734X     EISSN: 15537358     Source Type: Journal    
DOI: 10.1371/journal.pcbi.1002574     Document Type: Article

References (45)

1. Xie L, Wang J, Bourne PE, (2007) In silico elucidation of the molecular mechanism defining the adverse effect of selective estrogen receptor modulators. PLoS Comput Biol 3: e217.
2. Scheiber J, Chen B, Milik M, Sukuru SCK, Bender A, et al. (2009) Gaining insight into off-target mediated effects of drug candidates with a comprehensive systems chemical biology analysis. J Chem Inf Model 49: 308-317.
3. Xie L, Li J, Xie L, Bourne PE, (2009) Drug discovery using chemical systems biology: identification of the protein-ligand binding network to explain the side effects of cetp inhibitors. PLoS Comput Biol 5: e1000387.
4. Ashburn TT, Thor KB, (2004) Drug repositioning: identifying and developing new uses for existing drugs. Nat Rev Drug Discov 3: 673-683.
5. O'Connor KA, Roth BL, (2005) Finding new tricks for old drugs: an efficient route for public-sector drug discovery. Nat Rev Drug Discov 4: 1005-1014.
6. Kinnings SL, Liu N, Buchmeier N, Tonge PJ, Xie L, et al. (2009) Drug discovery using chemical systems biology: repositioning the safe medicine comtan to treat multi-drug and extensively drug resistant tuberculosis. PLoS Comput Biol 5: e1000423.
7. Keiser MJ, Setola V, Irwin JJ, Laggner C, Abbas AI, et al. (2009) Predicting new molecular targets for known drugs. Nature 462: 175-181.
8. Dudley JT, Deshpande T, Butte AJ, (2011) Exploiting drug-disease relationships for computational drug repositioning. Brief Bioinform 12: 303-311.
9. Garcia-Serna R, Ursu O, Oprea TI, Mestres J, (2010) iphace: integrative navigation in pharmaco-logical space. Bioinformatics 26: 985-986.
10. Taboureau O, Nielsen SK, Audouze K, Weinhold N, Edsgrd D, et al. (2011) Chemprot: a disease chemical biology database. Nucleic Acids Res 39: D367-D372.
11. Kuhn M, Szklarczyk D, Franceschini A, Campillos M, von Mering C, et al. (2010) Stitch 2: an interaction network database for small molecules and proteins. Nucleic Acids Res 38: D552-D556.
12. Oprea TI, Nielsen SK, Ursu O, Yang JJ, Taboureau O, et al. (2011) Associating drugs, targets and clinical outcomes into an integrated network affords a new platform for computer-aided drug repurposing. Mol Inform 30: 100-111.
13. Li YY, An J, Jones SJM, (2011) A computational approach to finding novel targets for existing drugs. PLoS Comput Biol 7: e1002139.
14. Yang L, Wang K, Chen J, Jegga AG, Luo H, et al. (2011) Exploring off-targets and off-systems for adverse drug reactions via chemical-protein interactome-clozapine-induced agranulocytosis as a case study. PLoS Comput Biol 7: e1002016.
15. Nidhi, Glick M, Davies JW, Jenkins JL, (2006) Prediction of biological targets for compounds using multiple-category bayesian models trained on chemogenomics databases. J Chem Inf Model 46: 1124-1133.
16. Keiser MJ, Roth BL, Armbruster BN, Ernsberger P, Irwin JJ, et al. (2007) Relating protein pharmacology by ligand chemistry. Nat Biotechnol 25: 197-206.
17. Campillos M, Kuhn M, Gavin AC, Jensen LJ, Bork P, (2008) Drug target identification using side-effect similarity. Science 321: 263-266.
18. Lamb J, Crawford ED, Peck D, Modell JW, Blat IC, et al. (2006) The connectivity map: using gene-expression signatures to connect small molecules, genes, and disease. Science 313: 1929-1935.
19. Ferreira JD, Couto FM, (2010) Semantic similarity for automatic classification of chemical compounds. PLoS Comput Biol 6: e1000937.
20. Perlman L, Gottlieb A, Atias N, Ruppin E, Sharan R, (2011) Combining drug and gene similarity measures for drug-target elucidation. J Comput Biol 18: 133-145.
21. Bleakley K, Yamanishi Y, (2009) Supervised prediction of drug-target interactions using bipartite local models. Bioinformatics 25: 2397-2403.
22. Zhao S, Li S, (2010) Network-based relating pharmacological and genomic spaces for drug target identification. PLoS One 5: e11764.
23. Shadbolt N, Hall W, Berners-Lee T, (2006) The semantic web revisited. Intelligent Systems, IEEE 21: 96-101.
24. Chen B, Dong X, Jiao D, Wang H, Zhu Q, et al. (2010) Chem2bio2rdf: a semantic framework for linking and data mining chemogenomic and systems chemical biology data. BMC Bioinformatics 11: 255.
25. Samwald M, Jentzsch A, Bouton C, Kallese CS, Willighagen E, et al. (2011) Linked open drug data for pharmaceutical research and development. J Cheminform 3: 19.
26. Belleau F, Nolin MA, Tourigny N, Rigault P, Morissette J, (2008) Bio2RDF: Towards a mashup to build bioinformatics knowledge systems. J Biomed Inform 41: 706-716.
27. Wild DJ, Ding Y, Sheth AP, Harland L, Gifford EM, et al. (2011) Systems chemical biology and the semantic web: what they mean for the future of drug discovery research. Drug Discov Today 17: 469-74.
28. Jeh G, Widom J, (2002) Simrank: a measure of structural-context similarity. pp. 538-543 In: Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining; New York, NY, United States; ACM, KDD '02 doi:10.1145/775047.775126. URL http://doi.acm.org/10.1145/775047.775126.
29. Aleman-Meza B, Halaschek-Wiener C, Arpinar IB, Ramakrishnan C, Sheth AP, (2005) Ranking complex relationships on the semantic web. IEEE Internet Computing 9: 37-44.
30. Anyanwu K, Maduko A, Sheth A, (2005) Semrank: ranking complex relationship search results on the semantic web. pp. 117-127 In: Proceedings of the 14th international conference on World Wide Web; New York, NY, United States. ACM, WWW '05 doi:10.1145/1060745.1060766. URL http://doi.acm.org/10.1145/1060745.1060766.
31. Chen B, Ding Y, Wild DJ, (2012) Improving integrative searching of systems chemical biology data using semantic annotation. J Cheminform 4: 6.
32. Liben-Nowell D, Kleinberg J, (2007) The link-prediction problem for social networks. J Am Soc Inf Sci Technol 58: 1019-1031.
33. Yamanishi Y, Kotera M, Kanehisa M, Goto S, (2010) Drug-target interaction prediction from chemical, genomic and pharmacological data in an integrated framework. Bioinformatics 26: i246-i254.
34. Vidal D, Mestres J, (2010) In silico receptorome screening of antipsychotic drugs. Mol Inform 29: 543-551.
35. Gnther S, Kuhn M, Dunkel M, Campillos M, Senger C, et al. (2008) Supertarget and matador: resources for exploring drug-target relationships. Nucleic Acids Res 36: D919-D922.
36. Pentel PR, Asinger RW, Benowitz NL, (1985) Propranolol antagonism of phenylpropanolamine- induced hypertension. Clin Pharmacol Ther 37: 488-494.
37. Mestres J, Gregori-Puigjan E, Valverde S, Sol RV, (2008) Data completeness-the achilles heel of drug-target networks. Nat Biotechnol 26: 983-984.
38. Gaulton A, Bellis LJ, Bento AP, Chambers J, Davies M, et al. (2011) Chembl: a large-scale bioactivity database for drug discovery. Nucleic Acids Res 40: D1100-7.
39. Wang H, Ding Y, Tang J, Dong X, He B, et al. (2011) Finding complex biological relationships in recent pubmed articles using bio-lda. PLoS One 6: e17243.
40. He B, Tang J, Ding Y, Wang H, Sun Y, et al. (2011) Mining relational paths in integrated biomedical data. PLoS One 6: e27506.
41. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, et al. (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13: 2498-2504.
42. Fawcett T, (2006) An introduction to roc analysis. Pattern Recogn Lett 27: 861-874.
43. Jacob L, Vert JP, (2008) Protein-ligand interaction prediction: an improved chemogenomics ap- proach. Bioinformatics 24: 2149-2156.
44. Davis J, Goadrich M, (2006) The relationship between precision-recall and roc curves. pp. 233-240 In: Proceedings of the 23rd international conference on Machine learning; New York, NY, USA. ACM, ICML '06 doi:10.1145/1143844.1143874. URL http://doi.acm.org/10.1145/1143844.1143874.
45. Yildirim MA, Goh KI, Cusick ME, Barabsi AL, Vidal M, (2007) Drug-target network. Nat Biotechnol 25: 1119-1126.