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Hidden Markov Models (HMMs)

Hidden Markov Models (HMMs). (Lecture for CS498-CXZ Algorithms in Bioinformatics) Oct. 27, 2005 ChengXiang Zhai Department of Computer Science University of Illinois, Urbana-Champaign. Motivation: the CpG island problem. Methylation in human genome

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Hidden Markov Models (HMMs)

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  1. Hidden Markov Models (HMMs) (Lecture for CS498-CXZ Algorithms in Bioinformatics) Oct. 27, 2005 ChengXiang Zhai Department of Computer Science University of Illinois, Urbana-Champaign

  2. Motivation: the CpG island problem • Methylation in human genome • “CG” -> “TG” happens in most place except “start regions” of genes • CpG islands = 100-1,000 bases before a gene starts • Questions • Q1: Given a short stretch of genomic sequence, how would we decide if it comes from a CpG island or not? • Q2: Given a long sequence, how would we find the CpG islands in it?

  3. Answer to Q1: Bayes Classifier Hypothesis space: H={HCpG,HOther} Evidence: X=“ATCGTTC” Prior probability Likelihood of evidence (Generative Model) We need two generative models for sequences: p(X| HCpG), p(X|HOther)

  4. A Simple Model for Sequences:p(X) Probability rule Assume independence Capture some dependence P(x|HCpG) P(A|HCpG)=0.25 P(T|HCpG)=0.25 P(C|HCpG)=0.25 P(G|HCpG)=0.25 P(x|HOther) P(A|HOther)=0.25 P(T|HOther)=0.40 P(C|HOther)=0.10 P(G|HOther)=0.25 X=ATTG Vs. X=ATCG

  5. How can we identify a CpG island in a long sequence? Idea 1: Test each window of a fixed number of nucleitides Idea2: Classify the whole sequence Class label S1: OOOO………….……O Class label S2: OOOO…………. OCC … Class label Si: OOOO…OCC..CO…O … Class label SN: CCCC……………….CC S*=argmaxS P(S|X) = argmaxS P(S,X) S*=OOOO…OCC..CO…O CpG Answer to Q2: Hidden Markov Model X=ATTGATGCAAAAGGGGGATCGGGCGATATAAAATTTG Other CpG Island Other

  6. HMM is just one way of modeling p(X,S)…

  7. B I A simple HMM 0.8 0.8 Parameters Initial state prob: p(B)= 0.5; p(I)=0.5 State transition prob: p(BB)=0.8 p(BI)=0.2 p(IB)=0.5 p(II)=0.5 Output prob: P(a|B) = 0.25, … p(c|B)=0.10 … P(c|I) = 0.25 … 0.5 0.5 P(B)=0.5 P(I)=0.5 0.2 0.2 P(x|B) P(x|I) 0.5 0.5 P(x|HCpG)=p(x|I) P(a|I)=0.25 P(t|I)=0.25 P(c|I)=0.25 P(g|I)=0.25 P(x|HOther)=p(x|B) P(a|B)=0.25 P(t|B)=0.40 P(c|B)=0.10 P(g|B)=0.25

  8. A General Definition of HMM Initial state probability: N states State transition probability: M symbols Output probability:

  9. B I How to “Generate” a Sequence? P(x|B) P(x|I) 0.8 0.5 P(a|B)=0.25 P(t|B)=0.40 P(c|B)=0.10 P(g|B)=0.25 P(a|I)=0.25 P(t|I)=0.25 P(c|I)=0.25 P(g|I)=0.25 0.2 model 0.5 P(B)=0.5 P(I)=0.5 a c g t t … Sequence B I I I B B I B states I I I B B I I B … … Given a model, follow a path to generate the observations.

  10. B I How to “Generate” a Sequence? P(x|B) P(x|I) 0.8 0.5 P(a|B)=0.25 P(t|B)=0.40 P(c|B)=0.10 P(g|B)=0.25 P(a|I)=0.25 P(t|I)=0.25 P(c|I)=0.25 P(g|I)=0.25 0.2 model 0.5 P(B)=0.5 P(I)=0.5 a c g t t … Sequence 0.2 0.5 0.5 0.5 B I I I B 0.5 0.25 0.25 0.25 0.25 0.4 t a c g t P(“BIIIB”, “acgtt”)=p(B)p(a|B) p(I|B)p(c|I)p(I|I)p(g|I)p(I|I)p(t|I)p(B|I)p(t|B)

  11. HMM as a Probabilistic Model Time/Index: t1 t2 t3 t4 … Data: o1 o2 o3 o4 … Sequential data Random variables/ process Observation variable: O1 O2 O3 O4 … Hidden state variable: S1 S2 S3 S4 … State transition prob: Probability of observations with known state transitions: Output prob. Joint probability (complete likelihood): Init state distr. Probability of observations (incomplete likelihood): State trans. prob.

  12. Three Problems 1. Decoding – finding the most likely path Given: model, parameters, observations (data) Compute: most likely states sequence 2. Evaluation – computing observation likelihood Given: model, parameters, observations (data) Compute: the likelihood to generate the observed data

  13. Three Problems (cont.) 3 Training – estimating parameters • Supervised Given: model architecture, labeled data ( data+state sequence) • Unsupervised Given : model architecture, unlabeled data Maximum Likelihood

  14. Problem I: Decoding/ParsingFinding the most likely path You can think of this as classification with all the paths as class labels…

  15. B I ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? What’s the most likely path? P(x|B) P(x|I) 0.8 P(a|B)=0.25 P(t|B)=0.40 P(c|B)=0.10 P(g|B)=0.25 0.5 P(a|I)=0.25 P(t|I)=0.25 P(c|I)=0.25 P(g|I)=0.25 0.2 0.5 P(B)=0.5 P(I)=0.5 a c g t t a t g

  16. B I Viterbi Algorithm: An Example 0.8 0.5 P(x|B) P(a|B)=0.251 P(t|B)=0.40 P(c|B)=0.098 P(g|B)=0.251 P(x|I) 0.2 P(a|I)=0.25 P(t|I)=0.25 P(c|I)=0.25 P(g|I)=0.25 0.5 P(B)=0.5 P(I)=0.5 t = 1 2 3 4 … a c g t … 0.5 0.8 0.8 0.8 B B B B 0.2 0.2 0.2 0.5 0.5 0.5 0.5 0.5 I 0.5 I 0.5 I I VP(B): 0.5*0.251 (B) 0.5*0.251*0.8*0.098(BB) … VP(I) 0.5*0.25(I) 0.5*0.25*0.5*0.25(II) … Remember the best paths so far 0.5

  17. Viterbi Algorithm Observation: Algorithm: (Dynamic programming) Complexity: O(TN2)

  18. Problem II: EvaluationComputing the data likelihood Another use of an HMM, e.g., as a generative model for discrimination Also related to Problem III – parameter estimation

  19. Data Likelihood: p(O|) t = 1 2 3 4 … a c g t … 0.5 0.8 0.8 0.8 B B B B 0.2 0.2 0.2 0.5 0.5 0.5 0.5 0.5 I 0.5 I 0.5 I I All HMM parameters In general, Complexity of a naïve approach? 0.5

  20. The Forward Algorithm Observation: Algorithm: Generating o1…ot with ending state si The data likelihood is Complexity: O(TN2)

  21. Forward Algorithm: Example t = 1 2 3 4 … a c g t … 0.5 0.8 0.8 0.8 B B B B 0.2 0.2 0.2 0.5 0.5 0.5 0.5 0.5 I 0.5 I 0.5 I I 1(B): 0.5*p(“a”|B) 2(B): [1(B)*0.8+ 1(I)*0.5]*p(“c”|B) …… 1(I): 0.5*p(“a”|I) 2(I): [1(B)*0.2+ 1(I)*0.5]*p(“c”|I) …… P(“a c g t”) = 4(B)+ 4(I)

  22. The Backward Algorithm Observation: Algorithm: (o1…ot already generated) Starting from state si Generating ot+1…oT Complexity: O(TN2) The data likelihood is

  23. Backward Algorithm: Example t = 1 2 3 4 … a c g t … 0.5 0.8 0.8 0.8 B B B B 0.2 0.2 0.2 0.5 0.5 0.5 0.5 0.5 I 0.5 I 0.5 I I … … 4(B): 1 3(B): 0.8*p(“t”|B)*4(B)+ 0.2*p(“t”|I)*4(I) 3(I): 0.5*p(“t”|B)*4(B)+ 0.5*p(“t”|T)*4(I) 4(I): 1 P(“a c g t”) =  1(B)*1(B)+  1(I)* 1(I) =  2(B)*2(B)+  2(I)* 2(I)

  24. Problem III: TrainingEstimating Parameters Where do we get the probability values for all parameters? Supervised vs. Unsupervised

  25. Supervised Training Given: 1. N – the number of states, e.g., 2, (s1 and s2) 2. V – the vocabulary, e.g., V={a,b} 3. O – observations, e.g., O=aaaaabbbbb 4. State transitions, e.g., S=1121122222 Task: Estimate the following parameters 1. 1, 2 2. a11, a12,a22,a21 3. b1(a), b1(b), b2(a), b2(b) 1=1/1=1; 2=0/1=0 a11=2/4=0.5; a12=2/4=0.5 a21=1/5=0.2; a22=4/5=0.8 b1(a)=4/4=1.0; b1(b)=0/4=0; b2(a)=1/6=0.167; b2(b)=5/6=0.833 0.5 0.8 0.5 P(s1)=1 P(s2)=0 1 2 0.2 P(a|s1)=1 P(b|s1)=0 P(a|s2)=167 P(b|s2)=0.833

  26. Unsupervised Training Given: 1. N – the number of states, e.g., 2, (s1 and s2) 2. V – the vocabulary, e.g., V={a,b} 3. O – observations, e.g., O=aaaaabbbbb 4. State transitions, e.g., S=1121122222 Task: Estimate the following parameters 1. 1, 2 2. a11, a12,a22,a21 3. b1(a), b1(b), b2(a), b2(b) How could this be possible?  Maximum Likelihood:

  27. Intuition O=aaaaabbbbb,  P(O,q1|) P(O,qK|) P(O,q2|) q1=1111111111 q2=11111112211 … qK=2222222222 New ’ Computation of P(O,qk|) is expensive …

  28. Baum-Welch Algorithm Basic “counters”: Being at state si at time t Being at state si at time t and at state sj at time t+1 Computation of counters: Complexity: O(N2)

  29. Baum-Welch Algorithm (cont.) Updating formulas: Overall complexity for each iteration: O(TN2)

  30. What You Should Know • Definition of an HMM and parameters of an HMM • Viterbi Algorithm • Forward/Backward algorithms • Estimate parameters of an HMM in a supervised way

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