1. COT6930 Course Project

2. Outline Gene Selection Sequence Alignment

3. Why Gene Selection Identify marker genes that characterize different tumor status. Many genes are redundant and will introduce noise that lower performance. Can eventually lead to a diagnosis chip. (“breast cancer chip”, “liver cancer chip”)

4. Why Gene Selection

5. Gene Selection Methods fall into three categories: –Filter methods –Wrapper methods –Embedded methods Filter methods are simplest and most frequently used in the literature Wrapper methods are likely the most accurate ones

6. Filter Method Features (genes) are scored according to the evidence of predictive power and then are ranked. Top s genes with high score are selected and used by the classifier. –Scores: t-statistics, F-statistics, signal-noise ratio, … –The # of features selected, s, is then determined by cross validation. Advantage: Fast and easy to interpret.

7. Good versus bad features

8. Filter Method: Problem Genes are considered independently. –Redundant genes may be included. –Some genes jointly with strong discriminant power but individually are weak will be ignored. Good single features do not necessarily form a good feature set The filtering procedure is independent to the classifying method –Features selected can be applied to all types of classifying methods

9. Wrapper Method Iterative search: many “feature subsets” are scored base on classification performance and the best is used. –Select a good subset of features Subset selection: Forward selection, backward selection, their combinations. –Exhaustive searching is impossible. –Greedy algorithm are used instead.

10. Wrapper Method: Problem Computationally expensive –For each feature subset considered, the classifier is built and evaluated. Exhaustive searching is impossible –Greedy search only. Easy to overfit.

11. Embedded Method Attempt to jointly or simultaneously train both a classifier and a feature subset. Often optimize an objective function that jointly rewards accuracy of classification and penalizes use of more features. Intuitively appealing

12. Relief-F Relief-F a filter approach for feature selection –Relief

13. Relief-F Original Relief is only able to handle binary classification problem. Extension was made to handle multiple-class problem

14. Relief-F Categorical attributes Numerical attributes

15. Relief-F Problem Time Complexity –m ×(m×a+c×m×a+a)=O(cm 2 a) – Assume m=100, c=3, a=10,000 – Time complexity 300×10 6 Only considers one single attribute, cannot select a subset of “good” genes

16. Solution: Parallel Relief-F Version 1: –Clusters runs ReliefF in parallel, and updated weighted weight values are collected at the master. –Theoretical time complexity O(cm 2 a/p) P is the # of clusters

18. Parallel Relief-F Version 2: –Clusters runs ReliefF in parallel, and each cluster directly update the global weight values. –Each cluster also considers the current weight values to select nearest neighbour instances –Theoretical time complexity O(cm 2 a/p) p is the # of clusters

20. Parallel Relief-F Version 3 –Consider selecting a subset of important features –Comparing the difference between including/excluding a specific feature, and understand the importance of a gene with respect to an existing subset of features –Discussion in private!

21. Outline Gene Selection Sequence Alignment –Given a dataset D with N=1000 sequences (e.g., 1000 each) –Given an input x, –Do pair-wise global sequence alignment between x and all sequences D Dispatch jobs to clusters And aggregate the results