TY - JOUR TI - Biomedical Hypothesis Generation by Text Mining and Gene Prioritization AU - Petrič, Ingrid AU - Ligeti, Balázs AU - Győrffy, Balázs AU - Pongor, Sándor T2 - Protein and Peptide Letters AB - 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. DA - 2014/06/01/ PY - 2014 DP - IngentaConnect VL - 21 IS - 8 SP - 847 EP - 857 J2 - Protein and Peptide Letters KW - Biomedical hypothesis generation KW - disease gene prediction KW - gene prioritization KW - ovarian cancer KW - text mining ER -