Abstract
The massive, ever-growing literature in life science makes it increasingly difficult for individuals to grasp all the information relevant to their interests. Since even experts’ knowledge is likely to be incomplete, important findings or associations among key concepts may remain unnoticed in the flood of information. This paper brings and extends a formal model from information retrieval in order to discover those implicit, hidden knowledge. Focusing on the biomedical domain, specifically, gene-disease associations, this paper demonstrates that our proposed model can identify not-yet-reported genetic associations and that the model can be enhanced by existing domain ontology.
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References
Hearst, M.A.: Untangling text data mining. In: Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics, pp. 3–10 (1999)
Swanson, D.R.: Fish oil, Raynaud’s syndrome, and undiscovered public knowledge. Perspectives in Biology and Medicine 30(1), 7–18 (1986)
Weeber, M., Klein, H., de Jong-van den Berg, L.T.W., Vos, R.: Using concepts in literature-based discovery: simulating Swanson’s Raynaud-fish oil and migraine-magnesium discoveries. Journal of the American Society for Information Science and Technology 52(7), 548–557 (2001)
Aronson, A.R.: Effective mapping of biomedical text to the UMLS metathesaurus: The MetaMap program. In: Proceedings of American Medical Informatics 2001 Annual Symposium, pp. 17–21 (2001)
Srinivasan, P.: Text mining: generating hypotheses from Medline. Journal of the American Society for Information Science and Technology 55(5), 396–413 (2004)
Turtle, H., Croft, W.B.: Evaluation of an inference network-based retrieval model. ACM Transactions on Information Systems 9(3), 187–222 (1991)
Perez-Iratxeta, C., Wjst, M., Bork, P., Andrade, M.: G2D: a tool for mining genes associated with disease. BMC Genetics 6(1), 45 (2005)
Becker, K.G., Barnes, K.C., Bright, T.J., Wang, S.A.: The genetic association database. Nature Genetics 36, 431–432 (2004)
Fawcett, T.: ROC graphs: Notes and practical considerations for researchers. Technical Report HPL-2003-4, HP Laboratories (2004)
Resnik, P.: Semantic similarity in a taxonomy: An information-based measure and its application to problems of ambiguity in natural language. Journal of Artificial Intelligence Research 11, 95–130 (1999)
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Seki, K., Mostafa, J. (2007). Literature-Based Discovery by an Enhanced Information Retrieval Model. In: Corruble, V., Takeda, M., Suzuki, E. (eds) Discovery Science. DS 2007. Lecture Notes in Computer Science(), vol 4755. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75488-6_18
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DOI: https://doi.org/10.1007/978-3-540-75488-6_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-75487-9
Online ISBN: 978-3-540-75488-6
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