A two-phase ANN method for genome-wide detection of hormone response elements

Authors:
Maria Stepanova;Feng Lin;Valerie C.-L. Lin
Affiliations:
Bioinformatics Research Centre, Nanyang Technological University, Singapore;School of Computer Engineering, Nanyang Technological University, Singapore;School of Biological Sciences, Nanyang Technological University, Singapore
Venue:
PRIB'07 Proceedings of the 2nd IAPR international conference on Pattern recognition in bioinformatics
Year:
2007

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Abstract

Steroid hormone receptors compose a subgroup of regulatory proteins which tend to recognize partially symmetric response elements on DNA. Identification of the members of a gene regulatory machine conducted by steroid hormones could provide better understanding of nature and development of diseases. We present an approach based on a succession of neural networks, which can be used for highly specific detection of binding signals. It exploits the capability of a feed-forward neural network to model datasets with high confidence, while a recurrent network grants putative response elements with biologically meaningful structures. We have used a novel method to train such a two-phase artificial neural network with a set of experimentally validated response elements for steroid hormone receptors. We have demonstrated that sequence-based prediction followed by structure-based classification of putative binding sites allows to eliminate large amount of false positives. An implementation of the neural network with Field-Programmable Gate Array is also briefly described.