In general, a process has a Markov property if the probability of its future states depend only upon its present state and a fixed number of past states. Markov chain is a discrete random process with Markovian property. Markov chains often represented as directed graphs. For example, a DNA sequence can be shown as this Markov Model: Fig. Markov Model of a DNA sequence. Any path through the models from the start state to the end state will produce (emit) a DNA sequence. B: start state; E: end state Components of a Markov Model: States (nodes on the graph)Transition probabilities (how one state changes to another state, shown as arrows on the graph)Emission probabilities Profile HMMs is only one possible use of HMMs in bioinformatic applications. Similar to the example of DNA sequence model above, it is possible to define a Markov Model that summarizes information in a multiple alignment. Such model is referred to as profile HMMand is a generalization of PSSM, in particular because it allows position specific treatments of indels (gaps). Fig. A complete profile HMM. [Source] The i-th residue of a protein can be emitted from multiple alternative states. Since the residue itself does not carry information about which state it originated from, the model is referred as a hidden Markov model.
Posted on: Fri, 02 Jan 2015 15:38:14 +0000
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