Gene Expression Analysis using Markov Chains extracted from RNNs

Abstract

This paper present a new approach for the analysis of gene expres- sion, by extracting a Markov Chain from trained Recurrent Neural Networks (RNNs). A lot of microarray data is being generated, since array technologies have been widely used to monitor simultaneously the expression pattern of thou- sands of genes. Microarray data is highly specialized, involves several variables in which are complex to express and analyze. The challenge is to discover how to extract useful information from these data sets. So this work proposes the use of RNNs for data modeling, due to their ability to learn complex temporal non-linear data. Once a model is obtained for the data, it is possible to ex- tract the acquired knowledge and to represent it through Markov Chains model. Markov Chains are easily visualized in the form of states graphs, which show the influences among the gene expression levels and their changes in time