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
 
 
Highlights
With the increasing advance of the genomic projects, a great amount of microarray data has been generated
The temporal relation in the form of a Markov Chain allows the scientist to understand and predict under which conditions the organism changes its linear network of gene expression levels, and how it adapts to adverse conditions
Our work presents a first approach to solve the automatic extraction of such information and its application to the Stanford Microarray Database (SMD) [15]
Summary
With the increasing advance of the genomic projects, a great amount of microarray data has been generated. It contains a lot of redundant data with correlated gene expression levels As this data will be used as the input parameter of a RNN, it makes a dimensional and noise reduction necessary, in order to obtain useful results in the process of Markov Chain extraction [11], [12]. The temporal relation in the form of a Markov Chain allows the scientist to understand and predict under which conditions the organism changes its linear network of gene expression levels, and how it adapts to adverse conditions. Different environment conditions need different linear influences among genes, even to the degree that a positive influence between two genes needs to turn to a negative influence Extracting this information without the use of computational tecniques is impracticable because of the great amount of data and genes.
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