1. Classification of microarray gene expression data using support vector machines (SVM)
A presentation on the topic
For CIS 595 Bioinformatics course
by Despina Kontos
Spring 2003 – Temple University

2. Overview… What are microarray gene expression data?
What are Support Vectors Machines?
How can we use them to utilize these gene expression data?
CLASSIFICATION EXPERIMENTS !!!

3. Microarrays…
What are they anyway?? Gene expression levels on tissue or cell for varying environment conditions

4. Microarrays… From a machine learning point of view…
Genes
Experiment
g-1
g-2 ……
g-n
ex-1
ex-2
…….
ex-m
Tissue classification
Function classification

5. Support Vector Machines (SVM)
Linear classifiers
Attempt to avoid overfitting by finding the optimal hyperplane that separates the data
HOW???
By maximizing the Margin..
Support Vectors
Introduced by V.Vapnic and co-workers in 1995

6. Support Vector Machines (SVM)
And what about datasets that are not linearly separable??
Map the data into higher dimensional space and make linear
classification there (theorem!!)

7. Support Vector Machines (SVM)
Some mathematical formulations…
We need ONLY the support vectors for computations!!
We can use KERNEL functions to avoid computations in higher dimensional space

8. Some experiments…
M.P.S.Brown, W.N.Grundy, D.Lin, N.Cristianini, C.W.Sugnet, T.S.Furey, M.Ares Jr. and D.Haussler,“Knowledge-based analysis of microarray gene expression data by using support vector machines", Proc.Natl.Acad.Sci.USA,97, 1, pp , 2000.
Classification of gene function from microarray data using SVM
2,476 genes
79 DNA hybridization experiments
6 gene function families
Genes
Experiment
g-1
g-2 ……
g-n
ex-1
ex-2
…….
ex-m
SVM provided optimal classification!!!
F1 F2 F3 ...
Function Classification

9. Gene expression data on tissue
More experiments…
T.furey, N.Cristianini, N. Duffy, D. Bednarski, M. Schummer and D Haussler, “Support Vector Machine Classification and Validation of Cancer Tissue Samples Using Microarray Expressioin Data”, Bioinformatics, 2000.
Gene expression data on tissue
97,802 DNA clones
31 tissue samples
Cancer ovarian
Normal ovarian
Normal non-ovarian
Genes
Experiment
g-1
g-2 ……
g-n
ex-1
ex-2
…….
ex-m
Cancer
Not Cancer
...
Tissue
Classification

10. Conclusions
Microarray gene expression data are a very useful format of biological information (..expensive to obtain!!)
SVM new and very promising classification apprach
A lot of research still to be done on Biological information processing using techniques developed in fields such as Machine Learning, Data Mining, etc..

11. Additional resources.. N. Cristianini. ICML'01 tutorial, 2001
Osuna, R. Freund, and F. Girosi. Support vector machines: Training and applications.
In A.I. Memo. MIT A.I. Lab, 1996
N. Cristianini. ICML'01 tutorial, 2001

12. THANK YOU!!!!!