An Algorithm to Improve Disease Discrimination based on Gene Interactions

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A strong statistical interactions is commonly associated with diseases.  Disease classification could be significantly increased by our understanding of the gene interactions, especially when gene expression differs across different classes are not good enough, it is more important to take use of gene interactions for disease classification analyses. However, most gene selection algorithms in classification analyses merely focus on genes whose expression levels show differences across classes, and ignore the discriminatory information from gene interactions. In this study performed by Ji-Gang Zhang et al,  a two-stage algorithm has  that can take gene interaction into account during a gene selection procedure has been developed. Its best advantage is that it can take advantage of discriminatory information from gene interactions as well as gene expression differences, by using “Bayes error” as a gene selection criterion. Using simulated and real microarray data sets,they also demonstrated the ability of gene interactions for improvement in classification accuracy , and present that the proposed algorithm can yield small informative sets of genes while leading to highly accurate classification results.