title: Introduction to Hidden Markov Models and Its Applications in Biology.
abstract: A number of real-world systems have common underlying patterns among them and deducing these patterns is important for us in order to understand the environment around us. These patterns in some instances are apparent upon observation while in many others especially those found in nature are well hidden. Moreover, the inherent stochasticity in these systems introduces sufficient noise that we need models capable to handling it in order to decipher the underlying pattern. Hidden Markov model (HMM) is a probabilistic model that is frequently used for studying the hidden patterns in an observed sequence or sets of observed sequences. Since its conception in the late 1960s it has been extensively applied in biology to capture patterns in various disciplines ranging from small DNA and protein molecules, their structure and architecture that forms the basis of life to multicellular levels such as movement analysis in humans. This chapter aims at a gentle introduction to the theory of HMM, the statistical problems usually associated with HMMs and their uses in biology.
authors.affiliation: School of Molecular and Cellular Biology, Faculty of Biological Sciences, University of Leeds, Leeds, LS2 9JT, UK. V.B.MahalingamShanmugiah@leeds.ac.uk.
authors.forename: M S
authors.initials: MS
authors.lastname: Vijayabaskar
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D006801:Humans; D008390:Markov Chains; D017422:Sequence Analysis, DNA / Q000379:methods*
keywords: Baum–Welch algorithm; Emission probability; Expectation maximization; Forward–backward procedure; Hidden Markov model; Pattern recognition; Transition probability; Viterbi algorithm