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Computational Biology, Part 4 Protein Coding Regions

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Computational Biology, Part 7 Supervised Machine Learning and Searching for Sequence Families Robert F. Murphy Copyright  2008-2009. All rights reserved.
www.cs.cmu.edu/~tom/pubs/ MachineLearning.pdf
What is Machine Learning? ,[object Object],[object Object],Tom Mitchell white paper
Fundamental Question of Machine Learning ,[object Object],[object Object],Tom Mitchell white paper
Why Machine Learning? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Tom Mitchell white paper
Why Machine Learning? ,[object Object],[object Object],[object Object]
Successful Machine Learning Applications ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Tom Mitchell white paper
Machine Learning Paradigms ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Supervised Learning ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Classification vs. Regression ,[object Object],[object Object]
Representation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Formal description ,[object Object],[object Object],[object Object],[object Object],[object Object],Courtesy Tom Mitchell
Inductive learning hypothesis ,[object Object],Courtesy Tom Mitchell
Hypothesis space ,[object Object],[object Object],[object Object]
- + ??? Simple two class problem Describe each image by features Train classifier
k-Nearest Neighbor (kNN) ,[object Object]
k-Nearest Neighbor (kNN) ,[object Object],Feature #1 (e.g.., ‘area’) Feature #2 (e.g.., roundness) + - + + + + + + - - - - - ?
k-Nearest Neighbor (kNN) ,[object Object],for k=3, nearest neighbors are So we label it +
Linear Discriminants ,[object Object],[object Object],area bright. + - + + + + - - - - - ?
Decision trees ,[object Object],Slide courtesy of Christos Faloutsos
Decision trees ,[object Object],50 40 Slide courtesy of Christos Faloutsos
Decision trees ,[object Object],Slide courtesy of Christos Faloutsos
Decision trees ,[object Object],Slide courtesy of Christos Faloutsos area<50 Y + round. <40 N - ... Y N ‘ area’ round. + - + + + + + + - - - - - ? 50 40
Support vector machines ,[object Object],Feature #1 (e.g.., ‘area’) Feature #2 (e.g.., roundness) + - + + + + + + - - - - - ? Slide courtesy of Christos Faloutsos
Support Vector Machines (SVMs) ,[object Object],area round. + - + + + + - - - - - ? Slide courtesy of Christos Faloutsos
Support Vector Machines (SVMs) ,[object Object],area round. + - + + + + - - - - - ? Slide courtesy of Christos Faloutsos
Support Vector Machines (SVMs) ,[object Object],area round. + - + + + + - - - - - ? Slide courtesy of Christos Faloutsos
Support Vector Machines (SVMs) ,[object Object],+ - + + + + - - - - - ? area round. Slide courtesy of Christos Faloutsos
Support Vector Machines (SVMs) ,[object Object],+ - + + + + - - - - - ? area round. Slide courtesy of Christos Faloutsos
Support Vector Machines (SVMs) ,[object Object],[object Object],area round. + - + + + + - - - - - ? Slide courtesy of Christos Faloutsos
Support Vector Machines (SVMs) ,[object Object],[object Object],[object Object]
Support Vector Machines (SVMs) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Cross-Validation ,[object Object],[object Object],[object Object],[object Object],[object Object]
Describing classifier errors ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Confusion matrix - binary True Predicted Positive Negative Positive True Positive False Negative Negative False Positive True Negative
Precision-recall analysis Vary classifier parameter to “loosen” some performance estimate: i.e., confidence Ideal performance
Describing classifier errors ,[object Object],[object Object],[object Object]
Confusion matrix – multi-class Overall accuracy = 98% True Class Output of the Classifier DNA ER Gia Gpp Lam Mit Nuc Act TfR Tub DNA 98 2 0 0 0 0 0 0 0 0 ER 0 100 0 0 0 0 0 0 0 0 Gia 0 0 100 0 0 0 0 0 0 0 Gpp 0 0 0 96 4 0 0 0 0 0 Lam 0 0 0 4 95 0 0 0 0 2 Mit 0 0 2 0 0 96 0 2 0 0 Nuc 0 0 0 0 0 0 100 0 0 0 Act 0 0 0 0 0 0 0 100 0 0 TfR 0 0 0 0 2 0 0 0 96 2 Tub 0 2 0 0 0 0 0 0 0 98
Ground truth ,[object Object],[object Object],[object Object]
Stating Goals vs. Approaches ,[object Object],[object Object]
Stating Goals vs. Approaches ,[object Object],[object Object]
Resources ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
 
Goals for sequence families ,[object Object],[object Object]
Possible Approaches ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
PSSMs ,[object Object],[object Object]
Learning PSSMs ,[object Object],[object Object],[object Object],[object Object]
Position Specific Iterated BLAST (PSI-BLAST) ,[object Object],[object Object],[object Object],[object Object]
Problems with PSSMs ,[object Object],[object Object],[object Object]
Cobbling ,[object Object],[object Object]
Cobbling ,[object Object]
Cobbling ,[object Object],[object Object]
Cobbler Illustration sequence of “most representative” family member scores from profiles of conserved motifs similarity scores for sequence from “most representative” family member
Family Pairwise Search ,[object Object]
Family Pairwise Search ,[object Object],[object Object]
Which method is best? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Comparison Protocol ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Evaluation Training Sequences Training Model Testing Sequences All other Sequences Known Family Members Searching Ranked List of Matches
Evaluation metric - ROC ,[object Object],[object Object]
Example of Evaluation for ROC 2 ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Sequences up to 2 non-family members
Protocol for Comparison of Methods ,[object Object],[object Object],[object Object]
Results BLAST FPS HMMER MAST BLAST FPS MAST HMMER BLAST
Conclusion ,[object Object],[object Object]
Comparison Protocol ,[object Object],[object Object],[object Object],[object Object]
Which is best (part 2)? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Comparison Protocol ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Results
Conclusions ,[object Object],[object Object]

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Computational Biology, Part 4 Protein Coding Regions

  • 1. Computational Biology, Part 7 Supervised Machine Learning and Searching for Sequence Families Robert F. Murphy Copyright  2008-2009. All rights reserved.
  • 3.
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  • 6.
  • 7.
  • 8.
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  • 14.
  • 15. - + ??? Simple two class problem Describe each image by features Train classifier
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  • 35. Confusion matrix - binary True Predicted Positive Negative Positive True Positive False Negative Negative False Positive True Negative
  • 36. Precision-recall analysis Vary classifier parameter to “loosen” some performance estimate: i.e., confidence Ideal performance
  • 37.
  • 38. Confusion matrix – multi-class Overall accuracy = 98% True Class Output of the Classifier DNA ER Gia Gpp Lam Mit Nuc Act TfR Tub DNA 98 2 0 0 0 0 0 0 0 0 ER 0 100 0 0 0 0 0 0 0 0 Gia 0 0 100 0 0 0 0 0 0 0 Gpp 0 0 0 96 4 0 0 0 0 0 Lam 0 0 0 4 95 0 0 0 0 2 Mit 0 0 2 0 0 96 0 2 0 0 Nuc 0 0 0 0 0 0 100 0 0 0 Act 0 0 0 0 0 0 0 100 0 0 TfR 0 0 0 0 2 0 0 0 96 2 Tub 0 2 0 0 0 0 0 0 0 98
  • 39.
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  • 53. Cobbler Illustration sequence of “most representative” family member scores from profiles of conserved motifs similarity scores for sequence from “most representative” family member
  • 54.
  • 55.
  • 56.
  • 57.
  • 58. Evaluation Training Sequences Training Model Testing Sequences All other Sequences Known Family Members Searching Ranked List of Matches
  • 59.
  • 60.
  • 61.
  • 62. Results BLAST FPS HMMER MAST BLAST FPS MAST HMMER BLAST
  • 63.
  • 64.
  • 65.
  • 66.
  • 68.

Editor's Notes

  1. Feature Calculation Lecture 3D IP workshop 2005 - R.F. Murphy
  2. Feature Calculation Lecture 3D IP workshop 2005 - R.F. Murphy
  3. Feature Calculation Lecture 3D IP workshop 2005 - R.F. Murphy
  4. Feature Calculation Lecture 3D IP workshop 2005 - R.F. Murphy
  5. ISAC Tutorial - 5/17/08 - Copyright (c) 2008, R.F. Murphy
  6. Feature Calculation Lecture 3D IP workshop 2005 - R.F. Murphy
  7. Feature Calculation Lecture 3D IP workshop 2005 - R.F. Murphy
  8. Feature Calculation Lecture 3D IP workshop 2005 - R.F. Murphy
  9. Feature Calculation Lecture 3D IP workshop 2005 - R.F. Murphy
  10. Feature Calculation Lecture 3D IP workshop 2005 - R.F. Murphy
  11. Feature Calculation Lecture 3D IP workshop 2005 - R.F. Murphy
  12. Feature Calculation Lecture 3D IP workshop 2005 - R.F. Murphy
  13. Feature Calculation Lecture 3D IP workshop 2005 - R.F. Murphy
  14. Feature Calculation Lecture 3D IP workshop 2005 - R.F. Murphy
  15. Feature Calculation Lecture 3D IP workshop 2005 - R.F. Murphy
  16. Feature Calculation Lecture 3D IP workshop 2005 - R.F. Murphy
  17. Feature Calculation Lecture 3D IP workshop 2005 - R.F. Murphy
  18. Feature Calculation Lecture 3D IP workshop 2005 - R.F. Murphy
  19. Feature Calculation Lecture 3D IP workshop 2005 - R.F. Murphy
  20. Feature Calculation Lecture 3D IP workshop 2005 - R.F. Murphy
  21. Feature Calculation Lecture 3D IP workshop 2005 - R.F. Murphy