Abstract
Literature-based discovery (LBD) is an emerging methodology for uncovering nonovert relationships in the online research literature. Making such relationships explicit supports hypothesis generation and discovery. Currently LBD systems depend exclusively on co-occurrence of words or concepts in target documents, regardless of whether relations actually exist between the words or concepts. We describe a method to enhance LBD through capture of semantic relations from the literature via use of natural language processing (NLP). This paper reports on an application of LBD that combines two NLP systems: BioMedLEE and SemRep, which are coupled with an LBD system called BITOLA. The two NLP systems complement each other to increase the types of information utilized by BITOLA. We also discuss issues associated with combining heterogeneous systems. Initial experiments suggest this approach can uncover new associations that were not possible using previous methods.
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References
Swanson, D.R.: Fish oil, Raynaud’s syndrome, and undiscovered public knowledge. Perspect Biol Med 30 (1986) 7–18
Swanson, D.R., Smalheiser, N.R.: An interactive system for finding complementary literatures: a stimulus to scientific discovery. Artif Intell 91 (1997) 183–203
Hristovski, D., Peterlin, B., Mitchell, J.A., Humphrey, S.M.: Using literature-based discovery to identify disease candidate genes. Int J Med Inform 74 (2005) 289–298
Hristovski, D., Stare, J., Peterlin, B., Dzeroski, S.: Supporting discovery in medicine by association rule mining in Medline and UMLS. Medinfo 10 (2001) 1344–1348
Weeber, M., Klein, H., Aronson, A.R., Mork, J.G., de Jong-van den Berg, L.T., Vos, R.: Text-based discovery in biomedicine: the architecture of the DAD-system. Proc AMIA Symp (2000) 903–907
Gordon, M.D., Lindsay, R.K.: Toward discovery support systems: a replication, re-examination, and extension of Swanson’s work on literature-based discovery of a connection between Raynaud’s and fish oil. J Am Soc Inf Sci 47 (1996) 116–128
Gordon, M.D., Dumais, S.: Using latent semantic indexing for literature based discovery. J Am Soc Inf Sci 49 (1998) 674–685
Wren, J.D.: Extending the mutual information measure to rank inferred literature relationships. BMC Bioinformatics 5 (2004) 145
Pratt, W., Yetisgen-Yildiz, M.: LitLinker: capturing connections across the biomedical literature. In Proceedings of the 2nd International Conference on Knowledge Capture. ACM Press, Sanibel Island, FL, USA (2003)
Fuller, S.S., Revere, D., Bugni, P.F., Martin, G.M.: A knowledgebase system to enhance scientific discovery: Telemakus. Biomed Digit Libr 1 (2004) 2
Hu, X.: Mining novel connections from large online digital library using biomedical ontologies. Libr Manage 26 (2005) 261–270
Srinivasan, P., Libbus, B.: Mining MEDLINE for implicit links between dietary substances and diseases. Bioinformatics 20 Suppl 1 (2004) I290–I296
Rindflesch, T.C., Fiszman, M.: The interaction of domain knowledge and linguistic structure in natural language processing: interpreting hypernymic propositions in biomedical text. J Biomed Inform 36 (2003) 462–477
Lussier, Y., Borlawsky, T., Rappaport, D., Liu, Y., Friedman, C.: PhenoGO: assigning phenotypic context to gene ontology annotations with natural language processing. Pac Symp Biocomput (2006). pp. 64–75
Weeber, M., Kors, J.A., Mons, B.: Online tools to support literature-based discovery in the life sciences. Brief Bioinform 6 (2005) 277–286
Aronson, A.R.: Effective mapping of biomedical text to the UMLS Metathesaurus: the MetaMap program. Proc AMIA Symp (2001) 17–21
Agrawal, R., Mannila, H., Srikant, R., Toivonen, H., Verkamo, A.I.: Fast discovery of association rules. In: Fayyad, U. (ed.): Advances in Knowledge Discovery and Data mining. MIT Press, Cambridge, MA (1996), pp. 307–328
Friedman, C., Alderson, P.O., Austin, J.H., Cimino, J.J., Johnson, S.B.: A general natural-language text processor for clinical radiology. J Am Med Inform Assoc 1 (1994) 161–174
Friedman, C., Shagina, L., Lussier, Y., Hripcsak, G.: Automated encoding of clinical documents based on natural language processing. J Am Med Inform Assoc 11 (2004) 392–402
Humphreys, B.L., Lindberg, D.A., Schoolman, H.M., Barnett, G.O.: The Unified Medical Language System: an informatics research collaboration. J Am Med Inform Assoc 5 (1998) 1–11
Friedman, C., Borlawsky, T., Shagina, L., Xing, H.R., Lussier, Y.A.: Bio-ontology and text: bridging the modeling gap. Bioinformatics 22 (2006) 2421–2429
McCray, A.T., Srinivasan, S., Browne, A.C.: Lexical methods for managing variation in biomedical terminologies. Proc Annu Symp Comput Appl Med Care (1994) 235–239
Smith, L., Rindflesch, T., Wilbur, W.J.: MedPost: a part-of-speech tagger for biomedical text. Bioinformatics 20 (2004) 2320–2321
Ahlers, C., Fiszman, M., Demner-Fushman, D., Lang, F.-M., Thomas, C.R.: Extracting semantic predications from Medline citations for pharmacogenomics. Pac Symp Biocomput (2007) 209–220
Quinn, N.P., Lang, A.E., Marsden, C.D.: Insulin-induced hypoglycaemia does not abolish chorea. J Neurol Neurosurg Psychiatry 45 (1982) 1169–1170
Ristow, M.: Neurodegenerative disorders associated with diabetes mellitus. J Mol Med 82 (2004) 510–529
Andreassen, O.A., Dedeoglu, A., Stanojevic, V., Hughes, D.B., Browne, S.E., Leech, C.A., Ferrante, R.J., Habener, J.F., Beal, M.F., Thomas, M.K.: Huntington’s disease of the endocrine pancreas: insulin deficiency and diabetes mellitus due to impaired insulin gene expression. Neurobiol Dis 11 (2002) 410–424
Camon, E., Magrane, M., Barrell, D., Lee, V., Dimmer, E., Maslen, J., Binns, D., Harte, N., Lopez, R., Apweiler, R.: The Gene Ontology Annotation (GOA) Database: sharing knowledge in Uniprot with Gene Ontology. Nucleic Acids Res 32 (2004) D262–D266
Borlawsky, T., Friedman, C., Lussier, Y.: Generating executable knowledge for evidence-based medicine using natural language and semantic processing. AMIA Annu Symp Proc (2006)
Fiszman, M., Rindflesch, T.C., Kilicoglu, H.: Abstraction summarization for managing the biomedical research literature. Proc HLTNAACL Workshop on Computational Lexical Semantics (2004) 76–83
Fiszman, M., Rindflesch, T., Kilicoglu, H.: Summarizing drug information in Medline citations. Proc AMIA Annu Symp (2006)
Van Blercom, N., Lasa, A., Verger, K., Masramón, X., Sastre, V.M., Linazasoro, G: Effects of gabapentin on the motor response to levodopa: a double-blind, placebo-controlled, crossover study in patients with complicated Parkinson disease. Clin Neuropharmacol 27 (2004) 124–128
Silverdale, M.A., Nicholson, S.L., Crossman, A.R., Brotchie, J.M.: Topiramate reduces levodopa-induced dyskinesia in the MPTP-lesioned marmoset model of Parkinson’s disease. Mov Disord 20 (2005) 403–409
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Hristovski, D., Friedman, C., Rindflesch, T.C., Peterlin, B. (2008). Literature-Based Knowledge Discovery using Natural Language Processing. In: Bruza, P., Weeber, M. (eds) Literature-based Discovery. Information Science and Knowledge Management, vol 15. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68690-3_9
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DOI: https://doi.org/10.1007/978-3-540-68690-3_9
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