Log-odd: A new method for improving hidden Markov model decoding for gene finding

Hidden Markov models (HMMs) are applied to many problems of computational Molecular Biology. In a predictive task, the HMM is endowed with a decoding algorithm in order to assign the most probable path of states, and in turn the class labeling, to an unknown sequence. In this paper, we have introduced a new decoding algorithm called (Log odd-Viterbi (LV)) for gene finding, which combines the log odd of posterior probability and Viterbi algorithms, to avoid the drawbacks of using only Viterbi\}, or Posterior algorithms, and also to avoid under flow problem. LV is a two step process: In the first step, the log odd of posterior probability is computed at each state using posterior decoding algorithm and then the best allowed path through the model is evaluated by Viterbi algorithm. Our simulation results show that our proposed LV has better performance than other existing algorithms in the computational biological problems such as predicting coding regions in prokaryotic DNA sequences.