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Markov Encoding for Detecting Signals in Genomic Sequences
April-June 2005 (vol. 2 no. 2)
pp. 131-142

Abstract—We present a technique to encode the inputs to neural networks for the detection of signals in genomic sequences. The encoding is based on lower-order Markov models which incorporate known biological characteristics in genomic sequences. The neural networks then learn intrinsic higher-order dependencies of nucleotides at the signal sites. We demonstrate the efficacy of the Markov encoding method in the detection of three genomic signals, namely, splice sites, transcription start sites, and translation initiation sites.

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Index Terms:
Genomic sequences, gene structure prediction, Markov chain, neural networks, splice sites, transcription start site, translation initiation site.
Citation:
Jagath C. Rajapakse, Loi Sy Ho, "Markov Encoding for Detecting Signals in Genomic Sequences," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 2, no. 2, pp. 131-142, April-June 2005, doi:10.1109/TCBB.2005.27
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