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.