Markov encoding for detecting signals in genomic sequences

Author

Rajapakse, Jagath C. ; Ho, Loi Sy

Author_Institution

Sch. of Comput. Eng., Nanyang Technol. Univ., Singapore

Volume

2

Issue

2

fYear

2005

Firstpage

131

Lastpage

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.

Keywords

Markov processes; biology computing; encoding; genetics; molecular biophysics; molecular configurations; neural nets; Markov encoding; genomic sequences; lower-order Markov models; neural networks; nucleotides; signal detection; splice sites; transcription start sites; translation initiation sites; Bioinformatics; Biological information theory; Biological system modeling; DNA; Encoding; Genomics; Neural networks; Sequences; Signal detection; Signal processing; Genomic sequences; Markov chain; gene structure prediction; neural networks; splice sites; transcription start site; translation initiation site.; 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;

fLanguage

English

Journal_Title

Computational Biology and Bioinformatics, IEEE/ACM Transactions on

Publisher

ieee

ISSN

1545-5963

Type

jour

DOI

10.1109/TCBB.2005.27

Filename

1438350