HMM-Lec8-091604

# HMM-Lec8-091604 - Hidden Markov model BioE 480 In general...

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Hidden Markov model BioE 480 Sept 16, 2004

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In general, we have Bayes theorem: P(X|Y) = P(Y|X)P(X)/P(Y) Event X: the die is loaded, Event Y: 3 sixes. Example: Assume we know that on average extracellular proteins have a slightly different a.a. composition than intracellular ones. Eg. More cysteines. How do we use this information to predict a new protein sequence x=x 1 x 2 …x n whether it is intracellular or extracellular. We first split the training examples from Swiss-Prot into intracellular and extracellular proteins, leaving aside those unclassifiable. We then estimate a set of frequencies for intraceullar proteins and a set of extracellular frequencies. Also estimate the probability that any new sequence is extracelluar, p ext and intracellular p int , called prior probabilites , because they are best guesses about a sequence before we actually see the sequence itself. int a q ext a q
We now have: Because we assume that every sequence must be either extracellular or intracelluar, we have: By Bayes’ theorem, This is the number we want: the posterior probability that a sequence is extracellular. It is our best guess after we have seen the data. More complicated: transmembrane proteins have both intra and extra cellular components. = = i x i ext x i i q x P q ext x P int ) int | ( , ) | ( int) | ( ) | ( ) ( int x P p ext x P p x P ext + = + = i x i ext x ext i ext x ext i i i q p q p q p x ext P int int ) | (

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Random Model R : For two sequences x and y , of lengths n and m . If x i is the i th symbol in x , and y i the i th symbol in y . Assume that letter a occurs independently with some frequency q a . The probability of the two sequences x and y is just the product of the probabilities of each amino acid: P(x,y|R) = Π q xi Π q yi An alternative model: Match Model M : Aligned pairs of residues occur with a joint probability P ab . Its value can be thought of as the probability that the resdiues a and b have each independently been derived from some unknown original residue c in their common ancester. c might be the same as a and/or b . The probability of the whole alignment is: P(x,y|M) = Π p xiyi The ratio of these two likelihoods is the odds ratio : P(x,y|M) / P(x,y|R) = Π p xiyi / ( Π q xi Π q yi )= Π p xiyi / q xi q yi To make this additive, we take the logarithm of this ratio, the log-odd ratio . S =
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HMM-Lec8-091604 - Hidden Markov model BioE 480 In general...

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