CISC 667 Intro to Bioinformatics Fall 2005 Support Vector Machines II

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CISC 667 Intro to Bioinformatics(Fall
2005)Support Vector Machines (II)

• Bioinformatics Applications

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Combining pairwise similarity with SVMs for
protein homology detection
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Experiment known protein families
Jaakkola, Diekhans and Haussler 1999
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Vectorization
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A measure of sensitivity and specificity
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ROC 1
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ROC 0.67
ROC 0
ROC receiver operating characteristic score is
the normalized area under a curve the plots true
positives as a function of false positives
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Performance Comparison (1)
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Using Phylogenetic Profiles SVMs
YAL001C E-value
Phylogenetic
profile 0.122 1 1.064 0 3.589 0 0.008 1 0.
692 1 8.49 0 14.79 0 0.584 1 1.567 0 0.3
24 1 0.002 1 3.456 0 2.135 0 0.142 1 0.0
01 1 0.112 1 1.274 0 0.234 1 4.562 0 3.9
34 0 0.489 1 0.002 1 2.421 0 0.112 1
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phylogenetic profiles and Evolution Patterns
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x

Impossible to know for sure if the gene followed
exactly this
evolution pattern
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Tree Kernel (Vert, 2002)

• For a phylogenetic profile x and an evolution
  pattern e
• P(e) quantifies how natural the pattern is
• P(xe) quantifies how likely the pattern e is the
  true history of the profile x
• Tree Kernel
• K tree(x,y) Se
  p(e)p(xe)p(ye)
• Can be proved to be a kernel
• Intuition two profiles get closer in the feature
  space when they have shared common evolution
  patterns with high probability.

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Tree-Encoded Profile (Narra Liao, 2004)
0.55
0.34
Post-order traversal
0.75
0.67
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0.33
0.5
1 0.33 0.67 0.34 0.5 0.75 0.55
1 1 0 1 0 0 0 1 1
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Using Support Vector Machines
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Kernel function
where r 0.10
Soft margin regularization C 1.50
L(?) ? ?i ? ½ ? ?i ?j yi yj (K(xi xj) ?ij
/C)
Coding scheme BIN21
Evaluation Q3 (P1P2P3)/N C (TP?TN
- FP ?FN) / ?( PP ?PN ?AP ?AN) SOV segment
overlap accuracy
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Design tertiary classifiers
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Nguyen Rajapakse, Genome Informatics 14
218-227 (2003)
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A two-stage SVM
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