Markov encoding for detecting signals in genomic sequences.
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.
Rajapakse
Jagath C
JC
BioInformatics Research Center, School of Computer Engineering, Nanyang Technological University, Singapore 639798. asjagath@ntu.edu.sg
Ho
Loi Sy
LS
D001483:Base Sequence; D002874:Chromosome Mapping / Q000379:methods*; D003062:Codon / Q000235:genetics; D008390:Markov Chains; D008957:Models, Genetic; D015233:Models, Statistical; D008969:Molecular Sequence Data; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D014176:Protein Biosynthesis / Q000235:genetics*; D050436:Regulatory Elements, Transcriptional / Q000235:genetics*; D017422:Sequence Analysis, DNA / Q000379:methods*
D003062:Codon