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### HMMs

Course: CIS 535, Fall 2009
School: UPenn
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Word Count: 266

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Markov Hidden Models Lyle Ungar, University of Pennsylvania Markov Model Sequence of states of observations of transition of emission E..g., exon, intron, E.g., AATCGGCGT Called emissions The Markov matrix Mij = p(Sj | Si) P(Ok|Sj) Sequence Probability Probability Lyle H Ungar, University of Pennsylvania 2 Markov Matrix properties Columns of M sum to one You must transition somewhere...

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Markov Hidden Models Lyle Ungar, University of Pennsylvania Markov Model Sequence of states of observations of transition of emission E..g., exon, intron, E.g., AATCGGCGT Called emissions The Markov matrix Mij = p(Sj | Si) P(Ok|Sj) Sequence Probability Probability Lyle H Ungar, University of Pennsylvania 2 Markov Matrix properties Columns of M sum to one You must transition somewhere Multiplying by M gives probilites of the state of the next item in the sequence P(Sj) 0.67 0.33 = = Mij P(Si) 0.4 0.7 0.1 0.6 0.3 0.9 Lyle H Ungar, University of Pennsylvania 3 Prokaryotic HMM Lyle H Ungar, University of Pennsylvania 4 Eukarotic HMM Lyle H Ungar, University of Pennsylvania 5 Hidden Markov Model Cant observe the states Need to estimate using HMM using an EM algorithm Baum-Welsh or forward-backward Given an HMM, for a new sequence, find the most likely states Done using dynamic Viterbi programming algorithm Lyle H Ungar, University of Pennsylvania 6 More Realistic HMMs Frame Shifts HMMs (GMMs) need more states Distribution of exon lengths is not geometric Generalized Example gene finders Genscan Lyle H Ungar, University of Pennsylvania 7 How well do they work? Define criteria for working well Base level, exon level or entire gene? Sn: Sensitivity = fraction of correct exons over actual exons Sp: Specificity = fraction of correct exons over predicted exons Lyle H Ungar, University of Pennsylvania 8 HMM accura...

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UPenn - CIS - 535
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UPenn - CIS - 535
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UPenn - CIS - 535
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