Chengwei LEI, Ph.D. Assistant Professor of Computer Science

1. A Random Walk Based Approachfor Improving Interaction Network and Increasing Prediction Accuracy Chengwei LEI, Ph.D. Assistant Professor of Computer Science Department of Electrical Engineering and Computer Science McNeese State University

2. What is Interaction Network • Interaction network is a network of nodes that are connected by features.

3. First Introduced in Biology • If the feature is a physical and molecular, the interaction network is molecular interactions usually found in cells.

4. Network View of Protein Interaction Network

6. Sounds familiar?

8. Sounds familiar?

9. Even In Mechanical Engineering

10. Real-world Classification Good Bad • Noisy data • Overfitting problem • Few true “driver” changes / vast number of “passenger” changes.

11. Current Methods Classifier Prediction

12. Current Methods Statistical test Pick the most significant ones Classifier Prediction

13. Problem? • Ignore the relationships between nodes/features/sensors

14. Our approach • Improve prognosis by combining • Node readout data • Node-node interaction networks

16. Classifier Prediction

17. Network Transformation Matrix

18. Network Transformation Matrix

19. Network Classifier Transformation Matrix Prediction

20. Transformation Matrix • Transformation matrix is generated by apply the Random Walk with Restart (RWR) algorithm on the Interaction network.

21. Random Walk • A random walk is a mathematical formalization of a path that consists of a succession of random steps.

22. Random Walk • A random walk is a mathematical formalization of a path that consists of a succession of random steps. • Random walk for one node on a graph G is a walk on G where the next node is chosen uniformly at random from the set of neighbors of the current node • when the walk is at node v, the probability to move in the next step to the neighbor u is Pvu = 1/d(v) for (v, u) is connected and 0 otherwise.

23. Random Walk

24. Random Walk Step 1 Step 1

25. Random Walk

26. Random Walk Step 2 Step 2

27. Random Walk

28. Random Walk

29. Random Walk

30. Random Walk Step 3

31. Random Walk Step 1 Step 2 Step 3 …… Step N

33. Random Walk with Restart • A random walker start from a node (v) with • uniform probability to visit its neighbors • fixed probability c to revisit the start node (v) • The probability for a random walker to be on node j after k times is • fijk(v) is the probability for a random walker to take path i to j at time k • Fj(v) at equilibrium is the probability for a random walker starting from node v to reach node j => Similarity between patient v and j

36. How about Two?

45. Experiments • Biology Data • Cancer prediction

46. Classification results

47. Wang’s Dataset Network Transformation Matrix

48. Wang’s Dataset 10144 286 10144 286 10144 7885 7885 7885 2259

49. 286 286 Good Bad 7885 7885 2259 T-test 1247

50. 286 Good Bad 286 7885 7885 2259 1678 T-test