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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(More)
The performance of the ab inito gene prediction approaches mostly depends on the effectiveness of detecting the splice sites. This paper addresses the problem of splice site detection using higher-order Markov models. The tenet of our approach is to brace the higher-order dependencies of a Markov model by a neural network that receives the inputs from(More)
Low-order Markov models are insufficient to represent hidden and complex features surrounding translation initiation sites (TISs). We present a neural network approach for detecting TISs of eukaryotes that combines lower-order models carrying biological knowledge, with nonlinearity, to capture higher-order nucleotide correlations at TISs and in the(More)
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