Abstract
We present a novel approach to encode inputs to neural networks for the recognition of transcription start sites in RNA polymerase II promoter regions. The approach is based on Markov models that represent TATA-box and Inr transcription binding sites, characterizing a promoter. The Markovian parameters are used as inputs to three neural networks which learn potential distant relationships between the nucleotides at promoter regions. Such an approach allows for incorporating the biological contextual information of the promoter sites into neural network systems and implementing higher-order Markov models of the promoters. Our experiments on a human promoter data set, available at [19], showed an increased correlation coefficient rate of 0.69 on average, which is better than the earlier reported best rate of 0.65 by NNPP 2.1 method.
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Promoter dataset: http://www.fruitfly.org/seq_tools/datasets /Human/promoter/
Genie dataset: http://www.fruitfly.org/seq_tools/datasets/Human/
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Ho, L., Rajapakse, J. Input encoding method for identifying transcription start sites in RNA polymerase II promoters by neural networks. Soft Comput 10, 331–337 (2006). https://doi.org/10.1007/s00500-005-0491-y
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DOI: https://doi.org/10.1007/s00500-005-0491-y