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Hidden Markov Models in Bioinformatics with Application to Gene Finding in Human DNA 308-761 Machine Learning Project

A Hidden Markov Model (HMM) is a stochastic model that captures the statistical properties of observed real world data. A good HMM accurately models the real world source of the observed data and has the ability to simulate the source. Machine Learning techniques based on HMMs have been successfully applied to problems including speech recognition, optical character recognition, and as we will examine, problems in computational biology. The main computational biology problems with HMM-based solutions are protein family profiling, protein binding site recognition and the problem that is the topic of this paper, gene finding in DNA.