Gene finding using Hidden Markov Model

Abstract

The objective of this study is to perfom mini review on Hidden Markov Models (HMMs) which is recently impodant and popular among bioinfomatics researchers and large no of software tools are based on this technique. The mathematical foundations of HMMs shall be comidered first in brief manner and then the gene identification application. In the case of gene identification process, HMM basically resolve three basic problems: First is the evaluation problem, in this it computes the probability that a particular HMM will generates a given sequence of obsenrations. Second is Decodng problem, in which it will uncover the most likely hidden state and Third is Leaming problem, it is used to adjust the model parameter and train the HMM to find an optimal model. Evaluation problem can be solved by using Forward and Backward algorithm, Decodng problems are solved by using Viterbi algorithm and posterior decodng algorithm andthen Leaming problems are solved through Viterbi t r a i q algorithm and Baum-Welch algorithm. Finally, some limitatiom of the current approaches and future drections are also reported. © 2012 Asian Network for Scientific Information.