DRiskKB: A large-scale disease-disease risk relationship knowledge base constructed from biomedical text

BMC Bioinformatics

Volumn 15, Issue 1, 2014, Pages

  Xu, Rong ^a   Li, Li ^a   Wang, QuanQiu ^b  

^a CASE WESTERN RESERVE UNIVERSITY   (United States)

^b ThinTek LLC   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ITERATIVE METHODS; KNOWLEDGE BASED SYSTEMS; SEMANTICS;

COMPUTATIONAL APPROACH; DISEASE ETIOLOGY; DISEASE PHENOTYPES; DRUG REPOSITIONING; LEARNED PATTERNS; RELATIONSHIP KNOWLEDGE; SEMANTIC RELATIONSHIPS; SYNTACTIC PATTERNS;

GENES;

ALGORITHM; ARTICLE; AUTOMATED PATTERN RECOGNITION; DRUG REPOSITIONING; HUMAN; KNOWLEDGE BASE; MEDLINE; NON INSULIN DEPENDENT DIABETES MELLITUS; OBESITY; PHENOTYPE; RISK FACTOR;

ALGORITHMS; DIABETES MELLITUS, TYPE 2; DRUG REPOSITIONING; HUMANS; KNOWLEDGE BASES; MEDLINE; OBESITY; PATTERN RECOGNITION, AUTOMATED; PHENOTYPE; RISK FACTORS;

EID: 84899480100     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-15-105     Document Type: Article

References (44)

1. Bilder RM, Sabb FW, Cannon TD, London ED, Jentsch JD, Parker DS, Freimer NB. Phenomics: the systematic study of phenotypes on a genome-wide scale. Neuroscience 2009, 164(1):30-42. 10.1016/j.neuroscience.2009.01.027, 2760679, 19344640.
2. Freimer N, Sabatti C. The human phenome project. Nat Genet 2003, 34(1):15-21. 10.1038/ng0503-15, 12721547.
3. Houle D, Govindaraju DR, Omholt S. Phenomics: the next challenge. Nat Rev Genet 2010, 11(12):855-866. 10.1038/nrg2897, 21085204.
4. Xu R, Li L, Wang Q. Towards building a disease-phenotype relationship knowledge base: large-scale extraction of disease-manifestation relationship from literature. Bioinformatics 2003, doi: 10.1093/bioinformatics/btt359.
5. Lee DS, Park J, Kay KA, Christakis NA, Oltvai ZN, Barabasi AL. The implications of human metabolic network topology for disease comorbidity. Proc Nat Acad Sci 2008, 105(29):8.
6. Oti M, Huynen MA, Brunner HG. Phenome connections. Trends Genet 2008, 24(3):103-106. 10.1016/j.tig.2007.12.005, 18243400.
7. Park J, Lee DS, Christakis NA, Barabasi AL. The impact of cellular networks on disease comorbidity. Mol Syst Biol 2009, 5(1):262-268. 2683720, 19357641.
8. Roque FS, Jensen PB, Schmock H, Dalgaard M, Andreatta M, Hansen T, Søeby K, Bredkjær S, Juul A, Werge T, Jensen LJ, Brunak S. Using electronic patient records to discover disease correlations and stratify patient cohorts. PLoS Comput Biol 2011, 7(8):e1002141. 10.1371/journal.pcbi.1002141, 3161904, 21901084.
9. Rzhetsky A, Wajngurt D, Park N, Zheng T. Probing genetic overlap among complex human phenotypes. Proc Nat Acad Sci 2007, 104(28):11694-11699. 10.1073/pnas.0704820104, 1906727, 17609372.
10. Guo X, Gao L, Wei C, Yang X, Zhao Y, Dong A. A computational method based on the integration of heterogeneous networks for predicting disease-gene associations. PloS one 2011, 6(9):e24171. 10.1371/journal.pone.0024171, 3166294, 21912671.
11. Hoehndorf R, Schofield PN, Gkoutos GV. PhenomeNET: a whole-phenome approach to disease gene discovery. Nucleic Acids Res 39(8):e119-e119.
12. Hwang T, Zhang W, Xie M, Liu J, Kuang R. Inferring disease and gene set associations with rank coherence in networks. Bioinformatics 2011, 27(19):2692-2699. 10.1093/bioinformatics/btr463, 21824970.
13. Li Y, Patra JC. Genome wide inferring gene phenotype relationship by walking on the heterogeneous network. Bioinformatics 2010, 26(9):1219-1224. 10.1093/bioinformatics/btq108, 20215462.
14. Vanunu O, Magger O, Ruppin E, Shlomi T, Sharan R. Associating genes and protein complexes with disease via network propagation. PLoS Comput Biol 2010, 6(1):e1000641. 10.1371/journal.pcbi.1000641, 2797085, 20090828.
15. Wu X, Jiang R, Zhang MQ, Li S. Network-based global inference of human disease genes. Mol Syst Biol 2008, 4(1):189-199. 2424293, 18463613.
16. Yang P, Li X, Wu M, Kwoh CK, Ng SK. Inferring gene-phenotype associations via global protein complex network propagation. PloS one 2011, 6(7):e21502. 10.1371/journal.pone.0021502, 3143124, 21799737.
17. Yao X, Hao H, Li Y, Li S. Modularity-based credible prediction of disease genes and detection of disease subtypes on the phenotype-gene heterogeneous network. BMC Syst Biol 2011, 5(1):79. 10.1186/1752-0509-5-79, 3130676, 21599985.
18. Lage K, Karlberg EO, Storling ZM, Olason PI, Pedersen AG, Rigina O, Brunak S. A human phenome-interactome network of protein complexes implicated in genetic disorders. Nat Biotechnol 2007, 25(3):309-316. 10.1038/nbt1295, 17344885.
19. van Driel MA, Bruggeman J, Vriend G, Brunner HG, Leunissen JA. A text-mining analysis of the human phenome. Eur J Hum Genet 2006, 14(5):535-542. 10.1038/sj.ejhg.5201585, 16493445.
20. Mestres J, Gregori-Puigjan E, Valverde S, Sol RV. Data completeness: the Achilles heel of drug-target networks. Nat Biotechnol 2008, 26(9):983-984. 10.1038/nbt0908-983, 18779805.
21. Ananiadou S, Pyysalo S, Tsujii JI, Kell DB. Event extraction for systems biology by text mining the literature. Trends Biotechnol 2010, 28(7):381-390. 10.1016/j.tibtech.2010.04.005, 20570001.
22. Cohen KB, Hunter L. Getting started in text mining. PLoS Comput Biol 2008, 4(1):e20. 10.1371/journal.pcbi.0040020, 2217579, 18225946.
23. Hunter L, Cohen KB. Biomedical language processing: perspective what's beyond PubMed?. Mol Cell 2006, 21(5):589. 10.1016/j.molcel.2006.02.012, 1702322, 16507357.
24. Xu R, Wang Q. Large-scale extraction of drug-disease treatment pairs from biomedical literature for drug repurposing. BMC Bioinformatics 2013, 14(1):181. 10.1186/1471-2105-14-181, 3702428, 23742147.
25. Liu YI, Wise PH, Butte AJ. The etiome: identification and clustering of human disease etiological factors. BMC Bioinformatics 2009, 10(Suppl 2):S14.
26. Fiszman M, Rosemblat G, Ahlers CB, Rindflesch TC. Identifying risk factors for metabolic syndrome in biomedical text. AMIA Annual Symposium Proceedings 249-249. American Medical Informatics Association.
27. Derry S, Loke YK, Aronson JK. Incomplete evidence: the inadequacy of databases in tracing published adverse drug reactions in clinical trials. BMC Med Res Methodol 2001, 1(1):7. 10.1186/1471-2288-1-7, 57741, 11591220.
28. Etzioni O, Cafarella M, Downey D, Popescu AM, Shaked T, Soderland S, Yates A. Unsupervised named-entity extraction from the web: an experimental study. Artif Intell 2005, 165(1):91-134.
29. Agichtein E, Gravano L. Snowball: Extracting relations from large plain-text collections. Proceedings of the fifth ACM conference on Digital libraries 2002, 85-94. ACM.
30. Brin S. Extracting patterns and relations from the world wide web. World Wide Web Databases 1999, 172-183.
31. Caporaso JG, William A, David AR, Cohen KB, Hunter L. Rapid pattern development for concept recognition systems: application to point mutations. J Bioinform Comput Biol 2007, 5(06):1233-1259.
32. Carlson A, Betteridge J, Kisiel B, Settles B, Hruschka ER, Mitchell TM. Toward an architecture for never-ending language learning. Proceedings of the Twenty-Fourth Conference on Artificial Intelligence 2010, 3-3. AAAI.
33. Nakashole N, Theobald M, Weikum G. Scalable knowledge harvesting with high precision and high recall. Proceedings of the fourth ACM international conference on Web search and data mining 2011, 227-236. ACM.
34. Riloff E, Jones R. Learning dictionaries for information extraction by multi-level bootstrapping. Proceedings of the National Conference on Artificial Intelligence 1999, 474-479. John Wiley and Sons LTD.
35. Thelen M, Riloff E. A bootstrapping method for learning semantic lexicons using extraction pattern contexts. Proceedings of the ACL-02 conference on Empirical methods in natural language, processing-Volume 10 2002, 214-221. Association for Computational Linguistics.
36. Xu R, Supekar K, Morgan A, Das A, Garber AM. Unsupervised method for automatic construction of a disease dictionary from a large free text collection. AMIA Annual Symposium Proceedings. Volume 2008 2008, 820-820. American Medical Informatics Association.
37. Xu R, Morgan A, Das A, Garber AM. 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.
38. Xu R, Das A, Garber AM. Unsupervised method for extracting machine understandable medical knowledge from a large free text collection. AMIA Annual Symposium Proceedings. Volume 2009 2009, 709-709. American Medical Informatics Association.
39. Chen Y, Zhang GQ, Xu R. Semi-supervised image classification for automatic construction of a health image library. Proceedings of the 2nd ACM SIGHIT symposium on International health informatics 2012, 111-120. ACM.
40. Klein D, Manning CD. Accurate unlexicalized parsing. Proceedings of the 41st Annual Meeting on Association for Computational Linguistics. Volume 1 2003, 423-430. Association for Computational Linguistics.
41. Manning CD, Raghavan P, Schutze H. Introduction to information retrieval (Vol. 1) 2008, Cambridge: Cambridge University Press.
42. Hamosh A, Scott AF, Amberger JS, Bocchini CA, McKusick VA. Online Mendelian Inheritance in Man (OMIM), a knowledge base of human genes and genetic disorders. Nucleic Acids Res 2005, 33(suppl 1):D514-D517. 539987, 15608251.
43. Hindorff LA. A Catalog of Published Genome-Wide Association Studies. Available at: http://www.genome.gov/gwastudies. Accessed [01/2012].
44. Xu R, Garten Y, Supekar K, Altman RB, Garber AM. Extracting Subject Demographics From Abstracts of Randomized Clinical Trials. Medinfo 2007: Proceedings of the 12th World Congress on Health (Medical) Informatics, Building Sustainable Health Systems 2007, 550-550. IOS Press.