High sensitivity technique for translation initiation site detection

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 surrounding coding and noncoding regions. The model consists of Markov chain models of low-order, a protein encoding model, neural networks, and a ribosome scanning model. The Markov models capture the local interaction of coding and noncoding regions while the protein encoding model utilizes evolutionary information of proteins to encode the downstream coding sequence of the TIS. With the incorporation of ribosome scanning model, the present method allows for incorporating the biological contextual information of potential TIS into the neural network based prediction system, thereby increasing number of correct recognitions considerably. A 3-fold cross-validation evaluation on the Pedersen and Nielsen dataset yielded an average 93.8% of sensitivity and 96.9% of specificity, indicating superior accuracy of the present system to the previous highest measures of 88.5% of sensitivity and 963% of specificity.