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Genetics and Molecular Biology

Print version ISSN 1415-4757

Abstract

SILVA, Scheila de Avila e; GERHARDT, Günther J.L.  and  ECHEVERRIGARAY, Sergio. Rules extraction from neural networks applied to the prediction and recognition of prokaryotic promoters. Genet. Mol. Biol. [online]. 2011, vol.34, n.2, pp. 353-360. ISSN 1415-4757.  http://dx.doi.org/10.1590/S1415-47572011000200031.

Promoters are DNA sequences located upstream of the gene region and play a central role in gene expression. Computational techniques show good accuracy in gene prediction but are less successful in predicting promoters, primarily because of the high number of false positives that reflect characteristics of the promoter sequences. Many machine learning methods have been used to address this issue. Neural Networks (NN) have been successfully used in this field because of their ability to recognize imprecise and incomplete patterns characteristic of promoter sequences. In this paper, NN was used to predict and recognize promoter sequences in two data sets: (i) one based on nucleotide sequence information and (ii) another based on stability sequence information. The accuracy was approximately 80% for simulation (i) and 68% for simulation (ii). In the rules extracted, biological consensus motifs were important parts of the NN learning process in both simulations.

Keywords : neural network; promoter; rule extraction.

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