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.
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Keywords
- Transcription Factor Binding Site
- Steroid Hormone Receptor
- Recurrent Neural Network
- Milk Protein Gene
- Hardware Acceleration
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Stepanova, M., Lin, F., Lin, V.C.L. (2007). A Two-Phase ANN Method for Genome-Wide Detection of Hormone Response Elements. In: Rajapakse, J.C., Schmidt, B., Volkert, G. (eds) Pattern Recognition in Bioinformatics. PRIB 2007. Lecture Notes in Computer Science(), vol 4774. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75286-8_3
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DOI: https://doi.org/10.1007/978-3-540-75286-8_3
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