Multilayer feedforward networks are universal approximators
Neural Networks
Bioinformatics: the machine learning approach
Bioinformatics: the machine learning approach
Effective hidden Markov models for detecting splicing junction sites in DNA sequences
Information Sciences: an International Journal
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Proceedings of the 5th International Conference on Intelligent Systems for Molecular Biology
Functional Site Prediction on the DNA sequence by Artificial Neural Networks
IJSIS '96 Proceedings of the 1996 IEEE International Joint Symposia on Intelligence and Systems
New techniques for extracting features from protein sequences
IBM Systems Journal - Deep computing for the life sciences
An equalized error backpropagation algorithm for the on-line training of multilayer perceptrons
IEEE Transactions on Neural Networks
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
Splice sites prediction of Human genome using length-variable Markov model and feature selection
Expert Systems with Applications: An International Journal
Journal of Biomedical Informatics
SCS: Signal, Context, and Structure Features for Genome-Wide Human Promoter Recognition
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Computational Biology and Chemistry
A metastate HMM with application to gene structure identification in eukaryotes
EURASIP Journal on Advances in Signal Processing - Special issue on genomic signal processing
StackTIS: A stacked generalization approach for effective prediction of translation initiation sites
Computers in Biology and Medicine
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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.