1. Support Vector Machine Data Mining Olvi L. Mangasarian with Glenn M. Fung, Jude W. Shavlik & Collaborators at ExonHit – Paris Data Mining Institute University of Wisconsin - Madison

2. What is a Support Vector Machine?  An optimally defined surface  Linear or nonlinear in the input space  Linear in a higher dimensional feature space  Implicitly defined by a kernel function  K(A,B)  C

3. What are Support Vector Machines Used For?  Classification  Regression & Data Fitting  Supervised & Unsupervised Learning

4. Principal Topics  Knowledge-based classification  Incorporate expert knowledge into a classifier  Breast cancer prognosis & chemotherapy  Classify patients on basis of distinct survival curves  Isolate a class of patients that may benefit from chemotherapy  Multiple Myeloma detection via gene expression measurements  Drug discovery based on gene macroarray expression  Joint work with ExonHit

5. Support Vector Machines Maximize the Margin between Bounding Planes A+ A-

6. Principal Topics  Knowledge-based classification (NIPS*2002)

7. Conventional Data-Based SVM

8. Knowledge-Based SVM via Polyhedral Knowledge Sets

9. Incoporating Knowledge Sets Into an SVM Classifier  This implication is equivalent to a set of constraints that can be imposed on the classification problem.  Suppose that the knowledge set: belongs to the class A+. Hence it must lie in the halfspace :  We therefore have the implication:

10. Numerical Testing The Promoter Recognition Dataset  Promoter: Short DNA sequence that precedes a gene sequence.  A promoter consists of 57 consecutive DNA nucleotides belonging to {A,G,C,T}.  Important to distinguish between promoters and nonpromoters  This distinction identifies starting locations of genes in long uncharacterized DNA sequences.

11. The Promoter Recognition Dataset Numerical Representation  Simple “1 of N” mapping scheme for converting nominal attributes into a real valued representation:  Not most economical representation, but commonly used.

12. The Promoter Recognition Dataset Numerical Representation  Feature space mapped from 57-dimensional nominal space to a real valued 57 x 4=228 dimensional space. 57 nominal values 57 x 4 =228 binary values

13. Promoter Recognition Dataset Prior Knowledge Rules  Prior knowledge consist of the following 64 rules:

14. Promoter Recognition Dataset Sample Rules where denotes position of a nucleotide, with respect to a meaningful reference point starting at position and ending at position Then:

15. The Promoter Recognition Dataset Comparative Algorithms  KBANN Knowledge-based artificial neural network [Shavlik et al]  BP: Standard back propagation for neural networks [Rumelhart et al]  O’Neill’s Method Empirical method suggested by biologist O’Neill [O’Neill]  NN: Nearest neighbor with k=3 [Cost et al]  ID3: Quinlan’s decision tree builder[Quinlan]  SVM1: Standard 1-norm SVM [Bradley et al]

16. The Promoter Recognition Dataset Comparative Test Results

17. Principal Topics  Breast cancer prognosis & chemotherapy

18. Kaplan-Meier Curves for Overall Patients: With & Without Chemotherapy

19. Breast Cancer Prognosis & Chemotherapy Good, Intermediate & Poor Patient Groupings (6 Input Features : 5 Cytological, 1 Histological) (Clustering: Utilizes 2 Histological Features &Chemotherapy) 253 Patients (113 NoChemo, 140 Chemo) Cluster 113 NoChemo Patients Use k-Median Algorithm with Initial Centers: Medians of Good1 & Poor1 69 NoChemo Good 44 NoChemo Poor 67 Chemo Good 73 Chemo Poor Good Poor Intermediate Cluster 140 Chemo Patients Use k-Median Algorithm with Initial Centers: Medians of Good1 & Poor1 Good1: Lymph=0 AND Tumor<2 Compute Median Using 6 Features Poor1: Lymph>=5 OR Tumor>=4 Compute Median Using 6 Features Compute Initial Cluster Centers

20. Kaplan-Meier Survival Curves for Good, Intermediate & Poor Patients 82.7% Classifier Correctness via 3 SVMs

21. Kaplan-Meier Survival Curves for Intermediate Group Note Reversed Role of Chemotherapy

22. Multiple Myeloma Detection  Multiple Myeloma is cancer of the plasma cell  Plasma cells normally produce antibodies  Out of control plasma cells produce tumors  When tumors appear in multiple sites they are called Multiple Myeloma  Dataset  105 patients: 74 with MM, 31 healthy  Each patient is represented by 7008 gene measurements taken from plasma cell samples  For each one of the 7008 gene measurements  Absolute Call (AC):  Absent (A), Marginal (M) or Present (P)  Average Difference (AD):  Positive or negative number

23. Multiple Myeloma Data Representation A  1 0 0 M  0 1 0 P  0 0 1 AMP  7008 X 3 = 21024 AD  7008 Total = 28,032 per patient 104 Patients: 74 MM + 31 Healthy 104 X 28,032 Data Matrix A

24. Multiple Myeloma 1-Norm SVM Linear Classifier  Leave-one-out-correctness (looc) = 100%  Average number of features used = 7 per fold  Total computing time for 105 folds = 7892 sec.  Overall number of features used in 105 folds= 7

25. Breast Cancer Treatment Response Joint with ExonHit - Paris (Curie Dataset)  35 patients treated by a drug cocktail  9 partial responders; 26 nonresponders  25 gene expressions out of 692, selected by Arnaud Zeboulon  Most patients had 3 replicate measurements  1-Norm SVM classifier selected 14 out of 25 gene expressions  Leave-one-out correctness was 80%  Greedy combinatorial approach selected 5 genes out of 14  Separating plane obtained in 5-dimensional gene-expression space  Replicates of all patients except one used in training  Average of replicates of patient left out used for testing  Leave-one-out correctness was 33 out of 35, or 94.2%

26. Separation of Convex Hull of Replicates of: 10 Synthetic Nonresponders & 4 Synthetic Partial Responders

27. Linear Classifier in 3-Gene Space 35 Patients with 93 Replicates 26 Nonresponders & 9 Partial Responders

28. Conclusion  New approaches for SVM-based classification  Algorithms capable of classifying data with few examples in very large dimensional spaces  Typical of microarray classification problems  Classifiers based on both abstract prior knowledge as well as conventional datasets  Identification of breast cancer patients that can benefit from chemotherapy  Useful tool for drug discovery