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. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Seki, K., & Mostafa, J. (2007). Literature-based discovery by an enhanced information retrieval model. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4755 LNAI, pp. 185–196). Springer Verlag. https://doi.org/10.1007/978-3-540-75488-6_18
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