Markov encoding for detecting signals in genomic sequences.

Jagath C Rajapakse; Loi Sy Ho

doi:10.1109/tcbb.2005.27

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Markov encoding for detecting signals in genomic sequences.

Jagath C Rajapakse·Loi Sy Ho

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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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Base Sequence Chromosome Mapping Codon Markov Chains Models, Genetic Models, Statistical Molecular Sequence Data Neural Networks, Computer Pattern Recognition, Automated Protein Biosynthesis Regulatory Elements, Transcriptional Sequence Analysis, DNA Journal Article

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Jagath C Rajapakse, Loi Sy Ho. Markov encoding for detecting signals in genomic sequences.. https://doi.org/10.1109/tcbb.2005.27

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