A semantic-based approach for mining undiscovered public knowledge from biomedical literature

2005 IEEE International Conference on Granular Computing

Volumn 2005, Issue , 2005, Pages 22-27

  Xiaohua, Hu ^a   Guangrong, Li ^b   Illhoi, Yoo ^a   Xiaodan, Zhang ^a   Xuheng, Xu ^a  

^a Drexel University   (United States)

^b HUNAN UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

HYPOTHESIS; PUBLIC KNOWLEDGE; RAYNAUD DISEASE; SEMANTIC BASED KNOWLEDGE DISCOVERY SYSTEM;

DATA MINING; DATA PROCESSING; KNOWLEDGE ENGINEERING; MEDIAN DIVIDERS; SEMANTICS;

BIOMEDICAL ENGINEERING;

EID: 33845339278     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/GRC.2005.1547229     Document Type: Conference Paper

References (14)

1. Agrawal, R. Mannila, H., Srikant, R., Toivonen, H., and Verkamo, A.I. 1995. Fast Discovery of Association Rules, Advances in Knowledge Discovery and Data Mining, U. Fayyad, et al., Editors. 1995, AAAI/MIT Press.
2. DiGiacome, R.A., Kremer, J.M. and Shah, D.M. 1989. Fish oil dietary supplementation is patients with Raynaud's phenomenon: A double-blind, controlled, prospective study, American Journal of Medicine, 8:158-164.
3. Gallai, V., Sarchielli, P., Coata, G., Firenze, C., Morucci, P., and Abbritti, G. 1992. Magnesium levels in migraing. Results in a group of juvenile patients. Headache, 32(3):32-35.
4. Gordon, M.D. and Lindsay, R.K. 1996. Toward discovery support systems: A replication, reexamination, and extension of Swanson's work on literature-based discovery of a connection between Raynaud's and fish oil. Journal of the American Society for Information Science, 47:116-128.
5. Hristovski, D., Stare, J., Peterlin, B. and Dzeroski, S. 2001. Supporting discovery in medicine by association rule mining in Medline and UMLS. Medinfo, 10(Pt 2): 1344-1348.
6. Joshi, R., Li, X.L., Ramachandaran, S. and Leong, T.Y. 2004. Automatic Model Structuring from Text using BioMedical Ontology, In American Association for Artificial Intelligence (AAAI) Workshop, pp. 74-79, San Jose, California.
7. Lindsay, R.K. and Gordon, M.D. 1999. Literature-based discovery by lexical statistics. Journal of the American Society for Information Science, 50(7): 574-587.
8. Pratt, W. and Yetisgen-Yildiz, M. 2003. LitLinker: capturing connections across the biomedical literature, K-CAP'03, pp. 105-112, Sanibel Island, FL.
9. Srinivasan, P. 2004. Text mining: Generating hypotheses from MEDLINE, Journal of the American Society for Information Science, 55(4):396-413.
10. Swanson, D.R. and Smalheiser, N.R. 1997. An interactive system for finding complementary literatures: A stimulus to scientific discovery. Artificial Intelligence, 91 (2): 183-203.
11. Swanson, D.R. 1988. Migraine and magnesium: eleven neglected connections. Perspectives in Biology and Medicine, 31(4):526-557.
12. Swanson, D.R. 1986b. Fish-oil, Raynaud's Syndrome, and undiscovered public knowledge. Perspectives in Biology and Medicine, 30(1):7-18.
13. Swanson, D.R., 1986a. Undiscovered public knowledge, Library Quarterly, 56(2):103-118.
14. Weeber, M., Vos, R., Klein, H., de Jong-van den Berg, L.T.W., Aronson, A., and Molema, G. 2003. Generating hypotheses by discovering implicit associations in the literature: A case report for new potential therapeutic uses for Thalidomide. Journal of the American Medical Informatics Association, 10(3):252-259.