Passage retrieval based hidden knowledge discovery from biomedical literature

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. (C) 2011 Elsevier Ltd. All rights reserved.