Human complex diseases are generally caused by the combined effect of multiple genes, and a single SNP is difficult to explain the pathogenesis of diseases. The detection of interactions between genes is important to gain a better understanding of the genetic mechanisms of human complex diseases.
We proposed a gene-based information gain method (GBIGM), which is based on the entropy and information gain theory and view all SNPs in a gene for detecting GGIs in case-control studies. For a gene, we defined an information gain rate (IGR) by comparing the entropy of the data with and without a gene’s information. We consider IGR as a measure of genetic contribution for disease for this gene. While considering two genes, the IGR can be determined by comparing the joint entropy and individual entropies. We have developed a public platform for gene interaction mining.
Access to the gene interaction mining tool
click HERE to access the tool.
The tool is supported by the nature computation laboratory (NCLAB) in the school of Computer Science and Technology, Harbin Institute of Technology.