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Abstract Details
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Activity Number:
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499
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Type:
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Topic Contributed
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Date/Time:
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Wednesday, August 1, 2012 : 10:30 AM to 12:20 PM
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Sponsor:
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WNAR
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| Abstract - #306285 |
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Title:
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Penalized Mixtures of Variable Order Markov Chains for Biological Sequence Data
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Author(s):
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Karin S Dorman*+
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Companies:
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Iowa State University
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Address:
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Iowa State University, Ames, IA, , USA
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Keywords:
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interpolated Markov model ;
mixture model
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Abstract:
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The interpolated Markov model (IMM) is a heuristic model for discrete sequences of random variables that has had success in speech recognition and biological sequence classification. The basic idea is to grow higher order Markov chains only as needed to predict the next nucleotide. If a substring is common, then the full substring or an even longer superstring is used for prediction. If it is uncommon, then a shorter suffix is used for prediction. The IMM is a type of mixture Markov chain, where each transition is generated from a mixture of chains of varying order. The IMM is actually a heuristic algorithm for estimating the mixing proportions. We present a penalized mixture model to estimate the weights and transition probabilities in a mathematical framework. We demonstrate its application in genetics, where it has extensive potential.
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