Finding Correlations Of Common Gene Expressions Of Multiple Genetic Diseases By Using Microarray Data
Abstract—The gene regulatory network (GRN) reveals the regulatory relationships among genes and can provide a systematic understanding of molecular mechanisms underlying biological processes. The idea studied here is to compare traditional analysis of microarray gene expression profiles and network analysis of GRN inferred using gene expression data. The target is to find, if there is, common gene patterns effective genetic diseases. In this work we applied statistical analysis and clustering methods like k-means and random forest to find common structures among disease gene profiles. The same data is used to create a GRN network and network theory methods applied to find network statistics to inspect underlying mechanism of gene regulatory network for genetic diseases.