Biomedical Hypothesis Generation by Text Mining and Gene Prioritization

Title:Biomedical Hypothesis Generation by Text Mining and Gene Prioritization

Volume: 21 Issue: 8

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

Affiliation:

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

Abstract: 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.

Cite this article as:

Petric Ingrid, Ligeti Balazs, Gyorffy Balazs and Pongor Sandor, Biomedical Hypothesis Generation by Text Mining and Gene Prioritization, Protein & Peptide Letters 2014; 21 (8) . https://dx.doi.org/10.2174/09298665113209990063