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- Title
Gene expression and protein-protein interaction data for identification of colon cancer related genes using f-information measures.
- Authors
Paul, Sushmita; Maji, Pradipta
- Abstract
One of the most important and challenging problems in functional genomics is how to select the disease genes. In this regard, the paper presents a new computational method to identify disease genes. It judiciously integrates the information of gene expression profiles and shortest path analysis of protein-protein interaction networks. While the $$f$$ -information based maximum relevance-maximum significance framework is used to select differentially expressed genes as disease genes using gene expression profiles, the functional protein association network is used to study the mechanism of diseases. An important finding is that some $$f$$ -information measures are shown to be effective for selecting relevant and significant genes from microarray data. Extensive experimental study on colorectal cancer establishes the fact that the genes identified by the integrated method have more colorectal cancer genes than the genes identified from the gene expression profiles alone, irrespective of any gene selection algorithm. Also, these genes have greater functional similarity with the reported colorectal cancer genes than the genes identified from the gene expression profiles alone. The enrichment analysis of the obtained genes reveals to be associated with some of the important KEGG pathways. All these results indicate that the integrated method is quite promising and may become a useful tool for identifying disease genes.
- Subjects
GENOMICS; GENE expression; PROTEIN-protein interactions; GENETICS of colon cancer; GENETIC disorders
- Publication
Natural Computing, 2016, Vol 15, Issue 3, p449
- ISSN
1567-7818
- Publication type
Article
- DOI
10.1007/s11047-015-9485-6