Gene Expression Analysis using Markov Chains extracted from RNNs

Authors

  • Igor Lorenzato Almeida Programa Interdisciplinar de Po ́s Graduac ̧a ̃o em Computac ̧a ̃o Aplicada - PIPCA
  • Denise Regina Pechmann Programa Interdisciplinar de Po ́s Graduac ̧a ̃o em Computac ̧a ̃o Aplicada - PIPCA
  • Adelmo Luis Cechin Programa Interdisciplinar de Po ́s Graduac ̧a ̃o em Computac ̧a ̃o Aplicada - PIPCA

DOI:

https://doi.org/10.19153/cleiej.10.2.8

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

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Published

2007-12-01

Issue

Vol. 10 No. 2 (2007): Regular Issue and Special Issue of Best Papers presented at CLEI 2006, Santiago, Chile

Section

Articles

License

CLEIej is supported by its home institution, CLEI, and by the contribution of the Latin American and international researchers community, and it does not apply any author charges whatsoever for submitting and publishing. Since its creation in 1998, all contents are made publicly accesibly. The current license being applied is a (CC)-BY license (effective October 2015; between 2011 and 2015 a (CC)-BY-NC license was used).