Markov Chain Inference From Microarray Data

Array technologies have made it straightforward to monitor simultaneously the expression pattern of thousands of genes. Thus, a lot of data is being generated and the challenge now is to discover how to extract useful information from these data sets. Microarray data is highly specialized, involves several variables in a non-linear and temporal way, demanding nonlinear recurrent free models, which are complex to formulate and to analyse in a simple way. Markov Chains are easily visualized in the form of graphs of states, which show the influences among the gene expression levels and their changes in time. In this work, we propose a new approach to microarray data analysis, by extracting a Markov Chain from Microarray Data. Two aspects are of interest for the researcher: the time evolution of the genic expression and their mutual influence in the form of regulatory networks.