Abstract:

Traditional methods of differentially expressed genes analysis used only one single dataset of a study, so they couldn't handle the heterogeneity between studies and lead to inconsistency of analysis results. To overcome the above shortcoming, we proposed the concept of integrationdriven exclusion, designed and implemented an algorithm to use several datasets of different studies. In this algorithm we presented a metaanalysis tool to identify differentially expressed genes. Using datasets GDS2490 and GDS2491 from GEO, we compared this algorithm with the method of significance analysis of microarrays. Experiment results show that the designed algorithm can identify differentially expressed genes accurately by integrationdriven excluding, handle heterogeneity between studies effectively. This algorithm provides a new approach to identify differentially expressed genes.

Key words: artificial intelligence, meta analysis, integration driven exclusion, significance analysis of microarrays, heterogeneity