Biomedical Hypothesis Generation by Text Mining and Gene Prioritization

Author(s): Ingrid Petric, Balazs Ligeti, Balazs Gyorffy, Sandor Pongor.

Journal Name: Protein & Peptide Letters

Volume 21 , Issue 8 , 2014

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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.

Keywords: Biomedical hypothesis generation, disease gene prediction, gene prioritization, ovarian cancer, text mining.

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Article Details

Year: 2014
Page: [847 - 857]
Pages: 11
DOI: 10.2174/09298665113209990063
Price: $65

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