A quantitative model for linking two disparate sets of articles in MEDLINE

Bioinformatics

Volumn 23, Issue 13, 2007, Pages 1658-1665

  Torvik, Vetle I ^a   Smalheiser, Neil R ^a  

^a UNIVERSITY OF ILLINOIS AT CHICAGO   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER PROGRAM; CONFERENCE PAPER; CONTROLLED STUDY; CORRELATION ANALYSIS; DATA MINING; GENETIC ASSOCIATION; LOGISTIC REGRESSION ANALYSIS; MATHEMATICAL COMPUTING; MEDICAL LITERATURE; MEDLINE; NORMAL DISTRIBUTION; PRIORITY JOURNAL; PROBABILITY; QUALITATIVE RESEARCH; SCORING SYSTEM;

EID: 34547852211     PISSN: 13674803     EISSN: 13674811     Source Type: Journal    
DOI: 10.1093/bioinformatics/btm161     Document Type: Conference Paper

References (37)

1. Agresti, A. (2002) Categorical Data Analysis. 2nd edn. Wiley, New York.
2. Aronson, A.R. (2001) Effective mapping of biomedical text to the UMLS Metathesaurus: The MetaMap program. Proc. AMIA Symp., 17-21.
3. Bennett, N.A. et al. (1999) Extracting noun phrases for all of MEDLINE. Proc. AMIA Symp., 671-675.
4. Bodenreider, O. (2004) The Unified Medical Language System (UMLS): integrating biomedical terminology. Nucleic Acids Res., 32, D267-D270.
5. Bowden, D.M. and Dubach, M.F. (2003) NeuroNames 2002. Neuroinformatics, 1, 43-59.
6. Chen, H. and Sharp, B.M. (2004) Content-rich biological network constructed by mining PubMed abstracts. BMC Bioinformatics, 5 147.
7. Cohen, A.M. and Hersh, W.R. (2005) A survey of current work in biomedical text mining. Brief Bioinform., 6, 57-71.
8. Hersh, W. et al. (2005). TREC 2005 Genomics track overview. In Voorhees, E.M. and Buckland, L.P. (eds.). NIST Special Publication 500-266: The Fourteenth Text Retrieval Conference (TREC 2005). National Institute of Standards and Technology, Gaithersburg, pp. 25-50.
9. Hersh, W.R. et al. (2006). Enhancing access to the Bibliome: The TREC 2004 Genomics Track. J. Biomed. Discovery Collaboration, 1 3.
10. Homayouni, R. et al. (2005) Gene clustering by latent semantic indexing of MEDLINE abstracts. Bioinformatics, 21, 104-115.
11. Hosmer, D.W. and Lemeshow, S. (2000) Applied Logistic Regression. 2nd edn. Wiley, New York.
12. Hristovski, D. et al. (2005) Using literature-based discovery to identify disease candidate genes, Int. J. Med. Inform., 74, 289-298.
13. Hunter, L. and Cohen, K.B. (2006) Biomedical language processing: What's beyond PubMed? Mol. Cell, 21, 589-594.
14. Jensen, L.J. et al. (2006) Literature mining for the biologist: from information retrieval to biological discovery. Nat. Rev. Genet. 7, 119-129.
15. Jenssen, T.K. et al. (2001) A literature network of human genes for high-throughput analysis of gene expression. Nat. Genet., 28, 21-28.
16. Kostoff, R.N. et al. (2004) Information content in Medline record fields, Int. J. Med. Inform., 73, 515-527.
17. Krallinger, M. and Valencia, A. (2005) Text-mining and information-retrieval services for molecular biology. Genome Biol. 6, 224.
18. Li, X. and Liu, B. (2005) Learning from positive and unlabeled examples with different data distributions. In Gama, J., et al. (eds.) Proceedings of the 16th European Conference Machine Learning (ECML 2005). Lecture Notes in Artificial Intelligence. Vol. 3720. Springer-Verlag Press, Berlin. pp. 218-229.
19. McCray, A.T. et al. (2001) Aggregating UMLS semantic types for reducing conceptual complexity. Medinfo., 10, 216-220.
20. Nakagawa, H. and Mori, T. (1998) Nested collocation and compound noun for term recognition. Proceedings of Workshop on Computational Terminology, Montreal, Canada. pp. 64-70.
21. Nigam, K. et al. (2000) Text classification from labeled and unlabeled documents using EM. Mach. Learn., 39, 103-134.
22. Ohta, T. et al. (2006) An intelligent search engine and GUI-based efficient MEDLINE search tool based on deep syntactic parsing. Proceedings of the COLING/ACL 2006 Interactive Presentation Sessions, Sydney, Australia. pp. 17-20.
23. Schuemie, M.J. et al. (2004) Distribution of information in biomedical abstracts and full-text publications. Bioinformatics, 20, 2597-2604,
24. Shah, P.K. et al. (2003) Information extraction from full text scientific articles: Where are the keywords? BMC Bioinformatics, 4, 20.
25. Smalheiser, N.R. (2005) The Arrowsmith project: 2005 status report. In Hoffmann, A. et al. (ed.) Discovery Science 2005. Lecture Notes in Artificial Intelligence. Vol. 3735, Springer-Verlag Press, Berlin, pp. 26-43.
26. Smalheiser, N.R. and Swanson, D.R. (1998) Using ARROWSMITH: A computer-assisted approach to formulating and assessing scientific hypotheses. Comput. Methods Programs Biomed., 57, 149-153.
27. Smalheiser, N.R. et al. (2006) Collaborative development of the Arrowsmith two-node search interface designed for laboratory investigators. J. Biomed. Discovery Collaboration, 1, 8.
28. Srinivasan, P. (2004) Text mining: Generating hypotheses from MEDLINE. J. Am. Soc. Information Sci. Technol., 55, 396-413.
29. Swanson, D.R. and Smalheiser, N.R. (1997) An interactive system for finding complementary literatures: A stimulus to scientific discovery. Artif. Int., 91, 183-203.
30. Swanson, D.R. et al. (2006) Ranking indirect connections in literature-based discovery: The role of Medical Subject Headings (MeSH). J. Am. Soc. Information Sci. Technol., 57, 1427-1439.
31. Tanabe, L. and Wilbur, W.J. (2004) Generation of a large gene/protein lexicon by morphological pattern analysis. J. Bioinform. Comput. Biol., 1, 611-626.
32. Voorhees, E.M. and Harman, D.K. (2005) TREC: Experiment and Evaluation in Information Retrieval. MIT Press, Cambridge, MA.
33. Weeber, M. et al. (2005) Online tools to support literature-based discovery in the life sciences. Brief Bioinform., 6, 277-286.
34. Wren, J.D. (2004) Extending the mutual information measure to rank inferred literature relationships. BMC Bioinformatics, 5, 145.
35. Wren, J.D. et al. (2004) Knowledge discovery by automated identification and ranking of implicit relationships. Bioinformatics 20, 389-398.
36. Yetisgen-Yildiz, M. and Pratt, W. (2006) Using statistical and knowledge-based approaches for literature-based discovery. J. Biomed. Inform., 39, 600-611.
37. Zhou, W. et al. (2006) ADAM: Another database of abbreviations in MEDLINE. Bioinformatics, 22, 2813-1818.