Hidden Markov Model Approaches for Biological Studies

Organism is a multi-level and modularized complex system that is composed of numerous interwoven metabolic and regulatory networks. Functional associations and random evolutionary events in evolution result in elusive molecular, physiological, metabolic, and evolutionary relationships. It is a daunting challenge for biological studies to decipher the complex biological mechanisms and crack the codes of life. Hidden Markov models and more generally hidden Markov random fields can capture both random signals and inherent correlation structure typically in time and space, and have emerged as a powerful approach to solve many analytical problems in biology. This article will introduce the theory of hidden Markov model and the computational algorithms for the three fundamental statistical problems and summarize striking applications of hidden Markov models to biological and medical studies.