Gene prioritization and clustering by multi-view text mining

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 in...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Shi Yu, Léon-Charles Tranchevent, Bart De Moor, Yves Moreau
• Keywords: BMCBI 2010, Disease Gene Identification, Text Mining, Text Mining Models