Automatic construction of a large-scale and accurate drug-side-effect association knowledge base from biomedical literature

Journal of Biomedical Informatics

Volumn 51, Issue , 2014, Pages 191-199

  Xu, Rong ^a   Wang, QuanQiu ^b  

^a CASE WESTERN RESERVE UNIVERSITY   (United States)

^b ThinTek LLC ^*  (United States)

Author keywords

Drug discovery; Drug repositioning; Drug side effect; Drug toxicity prediction; Text mining

Indexed keywords

DRUG INTERACTIONS; FORESTRY; KNOWLEDGE BASED SYSTEMS; TEXT MINING; TOXICITY;

AUTOMATIC CONSTRUCTION; BIOMEDICAL LITERATURE; CO-OCCURRENCE STATISTICS; DRUG DISCOVERY; DRUG REPOSITIONING; DRUG SIDE EFFECTS; TOXICITY PREDICTIONS; US FOOD AND DRUG ADMINISTRATION;

SUPPORT VECTOR MACHINES;

IRINOTECAN;

ADVERSE DRUG REACTION; ARTICLE; AUTOMATION; CASE STUDY; CLASSIFICATION ALGORITHM; CORRELATION ANALYSIS; DIAGNOSTIC ACCURACY; DRUG INDICATION; DRUG LABELING; DRUG METABOLISM; DRUG TARGETING; FOOD AND DRUG ADMINISTRATION; HUMAN; INTERMETHOD COMPARISON; KNOWLEDGE BASE; LEARNING; MACHINE LEARNING; MEDICAL LITERATURE; MEDLINE; NEUTROPENIA; PHARMACOGENETICS; PUBLICATION; SUPPORT VECTOR MACHINE; THROMBOCYTOPENIA; ARTIFICIAL INTELLIGENCE; BIOLOGICAL ONTOLOGY; CLASSIFICATION; CONTROLLED VOCABULARY; DRUG DATABASE; DRUG SURVEILLANCE PROGRAM; NATURAL LANGUAGE PROCESSING; ORGANIZATION AND MANAGEMENT; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; STATISTICS AND NUMERICAL DATA;

ADVERSE DRUG REACTION REPORTING SYSTEMS; ARTIFICIAL INTELLIGENCE; BIOLOGICAL ONTOLOGIES; DATABASES, PHARMACEUTICAL; DRUG-RELATED SIDE EFFECTS AND ADVERSE REACTIONS; HUMANS; NATURAL LANGUAGE PROCESSING; PERIODICALS AS TOPIC; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; VOCABULARY, CONTROLLED;

EID: 84908037538     PISSN: 15320464     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jbi.2014.05.013     Document Type: Article

References (37)

1. Ananiadou S., Pyysalo S., Tsujii J.I., Kell D.B. Event extraction for systems biology by text mining the literature. Trends Biotechnol 2010, 28(7):381-390.
2. Atias N., Sharan R. An algorithmic framework for predicting side-effects of drugs. J Comput Biol 2011, 18:207218.
3. Cami A., Arnold A., Manzi S., Reis B. Predicting adverse drug events using pharmacological network models. Sci Trans Med 2011, 3(114):114ra27.
4. 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.
5. Cohen K.B., Hunter L.E. Text mining for translational bioinformatics. PLoS Comput Biol 2013, 9(4):e1003044.
6. Cortes C., Vapnik V. Support-vector networks. Mach Learn 1995, 20(3):273-297.
7. Coulet A., Shah N.H., Garten Y., Musen M., Altman R.B. Using text to build semantic networks for pharmacogenomics. J Biomed Inform 2010, 43(6):1009-1019.
8. Fliri A.F., Loging W.T., Thadeio P.F., Volkmann R.A. Analysis of drug-induced effect patterns to link structure and side effects of medicines. Nature Chem Biol 2005, 1(7):389-397.
9. Fundel K., Kffner R., Zimmer R. RelExRelation extraction using dependency parse trees. Bioinformatics 2007, 23(3):365-371.
10. Gurulingappa H., Mateen-Rajput A., Toldo L. Extraction of potential adverse drug events from medical case reports. J Biomed Semantics 2012, 3(1):15.
11. Hall M., Frank E., Holmes G., Pfahringer B., Reutemann P., Witten I.H. The WEKA data mining software: an update. ACM SIGKDD Explor Newslett 2009, 11(1):10-18.
12. Hu G., Agarwal P. Human disease-drug network based on genomic expression profiles. PLoS One 2009, 4(8):e6536.
13. 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. 10.1038/clpt.2013.1.
14. Keiser M.J., Setola V., Irwin J.J., Laggner C., Abbas A.I., Hufeisen S.J., et al. Predicting new molecular targets for known drugs. Nature 2009, 462(7270):175-181.
15. Kilicoglu H., Bergler S. Syntactic dependency based heuristics for biological event extraction. Proceedings of the workshop on current trends in biomedical natural language processing: shared task 2009, 119-127. Association for Computational Linguistics.
16. Kinnings S.L., Liu N., Buchmeier N., Tonge P.J., Xie L., Bourne P.E. Drug discovery using chemical systems biology: repositioning the safe medicine Comtan to treat multi-drug and extensively drug resistant tuberculosis. PLoS Comput Biol 2009, 5(7):e1000423.
17. Klein D., Manning C.D. Accurate unlexicalized parsing. Proceedings of the 41st annual meeting on association for computational linguistics 2003, vol. 1:423-430. Association for Computational Linguistics.
18. Kuhn M., Campillos M., Letunic I., Jensen L.J., Bork P. A side effect resource to capture phenotypic effects of drugs. Mol Syst Biol 2010, 6(1).
19. Lamb J., Crawford E.D., Peck D., Modell J.W., Blat I.C., Wrobel M.J., et al. The connectivity map: using gene-expression signatures to connect small molecules, genes, and disease. Sci Signal 2006, 313(5795):1929.
20. Pauwels E., Stoven V., Yamanishi Y. Predicting drug side-effect profiles: a chemical fragment-based approach. BMC Bioinform 2011, 12(1):169.
21. Pouliot Y., Chiang A.P., Butte A.J. Predicting adverse drug reactions using publicly available PubChem BioAssay data. Clin Pharmacol Ther 2011, 90(1):90-99.
22. Shetty K.D., Dalal S.R. Using information mining of the medical literature to improve drug safety. J Am Med Inform Assoc 2011, 18(5):668-674.
23. Sirota M., Dudley J.T., Kim J., Chiang A.P., Morgan A.A., Sweet-Cordero A., et al. Discovery and preclinical validation of drug indications using compendia of public gene expression data. Sci Trans Med 2011, 3(96ra):77.
24. Whirl-Carrillo M., McDonagh E.M., Hebert J.M., Gong Li., Sangkuhl K., Thorn C.F., et al. Pharmacogenomics knowledge for personalized medicine. Clin Pharmacol Ther 2012, 92(4):414-417.
25. Wishart D.S., Knox C., Guo A.C., Shrivastava S., Hassanali M., Stothard P., et al. DrugBank: a comprehensive resource for in silico drug discovery and exploration. Nucl Acids Res 2006, 34(suppl 1):D668-D672.
26. Xie L., Li J., Xie L., Bourne P.E. Drug discovery using chemical systems biology: identification of the protein-ligand binding network to explain the side effects of CETP inhibitors. PLoS Comput Biol 2009, 5(5):e1000387.
27. Xie L., Evangelidis T., Xie L., Bourne P.E. Drug discovery using chemical systems biology: weak inhibition of multiple kinases may contribute to the anti-cancer effect of nelfinavir. PLoS Comput Biol 2011, 7(4):e1002037.
28. Xu R., Wang Q. A knowledge-driven conditional approach to extract pharmacogenomics specific drug-gene relationships from free text. J Biomed Inform 2012, 45(5):827-834.
29. Xu R., Wang Q. A semi-supervised approach to extract pharmacogenomics-specific drug-gene pairs from biomedical literature for personalized medicine. J Biomed Inform 2013, 46(4):585593.
30. Xu R., Wang Q. Automatic construction and integrated analysis of a cancer drug side effect knowledge base. J Am Med Inform Assoc 2013, 10.1136/amiajnl-2012-001584.
31. Xu R., Wang Q. Automatic signal prioritizing and filtering approaches in detecting post-marketing cardiovascular events associated with targeted cancer drugs from the FDA Adverse Event Reporting System (FAERS). J Biomed Inform 2014, 47:171-177. 10.1016/j.jbi.2013.10.008.
32. Xu R., Li L., Wang Q. Towards building a disease-phenotype relationship knowledge base: large scale extraction of disease-manifestation relationship from literature. Bioinformatics 2013, 10.1093/bioinformatics/btt359.
33. Xu R., et al. Unsupervised method for automatic construction of a disease dictionary from a large free text collection. AMIA annual symposium proceedings 2008, vol. 2008:820. American Medical Informatics Association.
34. Xu R., et al. Investigation of unsupervised pattern learning techniques for bootstrap construction of a medical treatment lexicon. Proceedings of the workshop on current trends in biomedical natural language processing 2009, 63-70. Association for Computational Linguistics.
35. Xu R., et al. Unsupervised method for extracting machine understandable medical knowledge from a large free text collection. AMIA annual symposium proceedings 2009, vol. 2009:709. American Medical Informatics Association.
36. Yang L., Wang K., Chen J., Jegga A.G., Luo H., Shi L., et al. Exploring off-targets and off-systems for adverse drug reactions via chemical-protein interactomeclozapine-induced agranulocytosis as a case study. PLoS Comput Biology 2011, 7(3):e1002016.
37. Yildirim M.A., Goh K.I., Cusick M.E., Barabsi A.L., Vidal M. Drugtarget network. Nat Biotechnol 2007, 25(10):1119.