A Machine Learning Based Method to Identify Differentially Expressed Genes

Detecting differentially expressed genes (DEGs) under two biological conditions is an essential step and is one of the most common reasons for statistical analysis of Microarray and RNA-seq data. There are various methods developed to detect DEGs either originate from a sophisticated statistical model based on fold-change (FC) strategy or from an analysis of biological reasoning. In this paper, we present a machine learning based method called Fusion for identifying DEGs based on an ensemble strategy, it provides a straightforward stringent way to determine the significance level for each gene. We use the Fusion technique on two biological datasets, the results show that in each case it performs more reliably and consistently than the Limma as well as other methods. A validation on the Platinum Spike dataset indicates that the proposed approach is more reliable with high confidence in identifying DEGs. An analysis of the biological function of the identified genes illustrates that the designed ensemble technique is powerful for identifying biologically relevant expression changes.