Literature-Based Discovery by an Enhanced Information Retrieval Model

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. Key words: Hypothesis discovery, Text data mining, Inference network, Implicit association, Gene Ontology

• Type: Conference
• Year: 2007
• Where: DIS
• Authors: Kazuhiro Seki, Javed Mostafa
• Keywords: DIS 2007, Ever-growing Literature, Key Concepts, Not-yet-reported Genetic Associations