MOTIVATION: Computational gene prioritization methods are useful to help 
identify susceptibility genes potentially being involved in genetic disease. 
Recently, text mining techniques have been applied to extract prior knowledge 
from text-based genomic information sources and this knowledge can be used to 
improve the prioritization process. However, the effect of various vocabularies, 
representations and ranking algorithms on text mining for gene prioritization is 
still an issue that requires systematic and comparative studies. Therefore, a 
benchmark study about the vocabularies, representations and ranking algorithms 
in gene prioritization by text mining is discussed in this article.
RESULTS: We investigated 5 different domain vocabularies, 2 text representation 
schemes and 27 linear ranking algorithms for disease gene prioritization by text 
mining. We indexed 288 177 MEDLINE titles and abstracts with the TXTGate text 
pro.ling system and adapted the benchmark dataset of the Endeavour gene 
prioritization system that consists of 618 disease-causing genes. Textual gene 
pro.les were created and their performance for prioritization were evaluated and 
discussed in a comparative manner. The results show that inverse document 
frequency-based representation of gene term vectors performs better than the 
term-frequency inverse document-frequency representation. The eVOC and MESH 
domain vocabularies perform better than Gene Ontology, Online Mendelian 
Inheritance in Man's and London Dysmorphology Database. The ranking algorithms 
based on 1-SVM, Standard Correlation and Ward linkage method provide the best 
performance.
AVAILABILITY: The MATLAB code of the algorithm and benchmark datasets are 
available by request.
SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics 
online.