title: Biomedical hypothesis generation by text mining and gene prioritization.
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.
authors.forename: Ingrid
authors.initials: I
authors.lastname: Petric
authors.forename: Balazs
authors.initials: B
authors.lastname: Ligeti
authors.forename: Balazs
authors.initials: B
authors.lastname: Gyorffy
authors.affiliation: Centre for Systems and Information Technologies, University of Nova Gorica, Vipavska 13, SI-5000 Nova Gorica, Slovenia. Ingrid.Petric@ung.si.
authors.forename: Sandor
authors.initials: S
authors.lastname: Pongor
mesh_terms: D000465:Algorithms; D014408:Biomarkers, Tumor / Q000235:genetics / Q000378:metabolism; D035843:Biomedical Research / Q000379:methods*; D019295:Computational Biology / Q000379:methods*; D057225:Data Mining / Q000379:methods*; D030541:Databases, Genetic; D004194:Disease / Q000235:genetics*; D005260:Female; D006801:Humans; D046650:Medical Subject Headings; D008954:Models, Biological; D010051:Ovarian Neoplasms / Q000235:genetics / Q000378:metabolism; D009626:Terminology as Topic
chemical_list: D014408:Biomarkers, Tumor