Augmenting HMM with neural network for finding gene structure
A probabilistic approach combining a hidden Markov model and neural networks is implemented to identify different functional entities in nucleotide sequences. This approach augments the Hidden Markov model probability parameters by using the outputs of neural networks. It is designed to capture the compositional properties of complex genes and thereby achieves low error rates and high correlation coefficient measures. Initial experiments demonstrate that the predictive efficiency of the model is considerably higher than the existing models of gene finding.