BACKGROUND: Text mining has become a useful tool for biologists trying to 
understand the genetics of diseases. In particular, it can help identify the 
most interesting candidate genes for a disease for further experimental 
analysis. Many text mining approaches have been introduced, but the effect of 
disease-gene identification varies in different text mining models. Thus, the 
idea of incorporating more text mining models may be beneficial to obtain more 
refined and accurate knowledge. However, how to effectively combine these models 
still remains a challenging question in machine learning. In particular, it is a 
non-trivial issue to guarantee that the integrated model performs better than 
the best individual model.
RESULTS: We present a multi-view approach to retrieve biomedical knowledge using 
different controlled vocabularies. These controlled vocabularies are selected on 
the basis of nine well-known bio-ontologies and are applied to index the vast 
amounts of gene-based free-text information available in the MEDLINE repository. 
The text mining result specified by a vocabulary is considered as a view and the 
obtained multiple views are integrated by multi-source learning algorithms. We 
investigate the effect of integration in two fundamental computational disease 
gene identification tasks: gene prioritization and gene clustering. The 
performance of the proposed approach is systematically evaluated and compared on 
real benchmark data sets. In both tasks, the multi-view approach demonstrates 
significantly better performance than other comparing methods.
CONCLUSIONS: In practical research, the relevance of specific vocabulary 
pertaining to the task is usually unknown. In such case, multi-view text mining 
is a superior and promising strategy for text-based disease gene identification.