Passage retrieval based hidden knowledge discovery from biomedical literature

Authors:
Ran Chen;Hongfei Lin;Zhihao Yang
Affiliations:
Department of Computer Science and Engineering, Dalian University of Technology, Dalian 116023, China;Department of Computer Science and Engineering, Dalian University of Technology, Dalian 116023, China;Department of Computer Science and Engineering, Dalian University of Technology, Dalian 116023, China
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

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Abstract

Biomedical literature is growing at a double-exponential pace and automatic extraction of the implicit biological relationship from biomedical literature contributes to building the biomedical hypothesis that can be explored further experimentally. This paper presents a passage retrieval based method which can explore the hidden connection from MEDLINE records. In this method, the MeSH concepts are retrieved from the sentence-level windows and are therefore more relevant with the starting term. This method is tested on three classical implicit connections: Alzheimer's disease and indomethacin, Migraine and Magnesium, Schizophrenia and Calcium-independent phospholipase A2 in the open discovery. In our experiments, three computational methods for scoring and ranking the MeSH terms are explored: z-score, TFIDF (Term Frequency Inverse Document Frequency) and PMI (pointwise mutual information). Experimental results show this method can significantly improve the hidden knowledge discovery performance.