Sciweavers

Share
CLEIEJ
2007

Gene Expression Analysis using Markov Chains extracted from RNNs

12 years 1 months ago
Gene Expression Analysis using Markov Chains extracted from RNNs
Abstract. This paper present a new approach for the analysis of gene expression, 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 thousands 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 extract 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.
Igor Lorenzato Almeida, Denise Regina Pechmann Sim
Added 12 Dec 2010
Updated 12 Dec 2010
Type Journal
Year 2007
Where CLEIEJ
Authors Igor Lorenzato Almeida, Denise Regina Pechmann Simon, Adelmo Luis Cechin
Comments (0)
books