Application of Hidden Markov Models in Bioinformatics
DU Shi-ping · College Mathematies · 2004
Hidden Markov Models(HMM) is a stochastic model that accurately captures the statistical properties of (observed) real world data. HMM is very well suited for many tasks in bioinformatics, although they have been successfully (applied) to speech recognition. This paper review the theory of HMM, and introduce the applications of HMM in biological sequence analysis, with a focus on the multiple alignment of DNA (sequence and genefinding.)