Literature-based discovery by an enhanced information retrieval model

Authors:
Kazuhiro Seki;Javed Mostafa
Affiliations:
Kobe University, Hyogo, Japan;Indiana University, Bloomington, IN
Venue:
DS'07 Proceedings of the 10th international conference on Discovery science
Year:
2007

Citing 4
Cited 1

Evaluation of an inference network-based retrieval model

ACM Transactions on Information Systems (TOIS) - Special issue on research and development in information retrieval
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
Text mining: generating hypotheses from MEDLINE

Journal of the American Society for Information Science and Technology
Untangling text data mining

ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics

Knowledge retrieval in the anatomical domain

Proceedings of the 1st ACM International Health Informatics Symposium

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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.