Biomedical Hypothesis Generation by Text Mining and Gene Prioritization

ISSN: 1875-5305 (Online)
ISSN: 0929-8665 (Print)

Volume 22 , 12 Issues, 2015

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Protein & Peptide Letters

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Prof. Ben M. Dunn
Department of Biochemistry and Molecular Biology
University of Florida
College of Medicine
P.O. Box 100245
Gainesville, FL

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Biomedical Hypothesis Generation by Text Mining and Gene Prioritization

Protein & Peptide Letters, 21(8): 847-857.

Author(s): 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.


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.


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

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Article Details

Volume: 21
Issue Number: 8
First Page: 847
Last Page: 857
Page Count: 11
DOI: 10.2174/09298665113209990063

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