Biomedical Hypothesis Generation by Text Mining and Gene Prioritization
Ingrid Petric, Balazs Ligeti, Balazs Gyorffy and Sandor Pongor
Affiliation: Centre for Systems and Information Technologies, University of Nova Gorica, Vipavska 13, SI-5000 Nova Gorica, Slovenia.
Keywords: Biomedical hypothesis generation, disease gene prediction, gene prioritization, ovarian cancer, text mining.
Text mining methods can facilitate the generation of biomedical hypotheses by suggesting novel associations
between diseases and genes. Previously, we developed a rare-term model called RaJoLink (Petric et al, J. Biomed.
Inform. 42(2): 219-227, 2009) in which hypotheses are formulated on the basis of terms rarely associated with a
target domain. Since many current medical hypotheses are formulated in terms of molecular entities and molecular
mechanisms, here we extend the methodology to proteins and genes, using a standardized vocabulary as well as a
gene/protein network model. The proposed enhanced RaJoLink rare-term model combines text mining and gene prioritization
approaches. Its utility is illustrated by finding known as well as potential gene-disease associations in ovarian
cancer using MEDLINE abstracts and the STRING database.
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