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KDD CUP 2001 Task 1: Thrombin

KDD CUP 2001 Task 1: Thrombin. Jie Cheng (Homepage: www.cs.ualberta.ca/~jcheng) Global Analytics Canadian Imperial Bank of Commerce. Overview. Objective Prediction of molecular bioactivity for drug design -- binding to Thrombin Data

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KDD CUP 2001 Task 1: Thrombin

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  1. KDD CUP 2001 Task 1:Thrombin Jie Cheng(Homepage: www.cs.ualberta.ca/~jcheng) Global Analytics Canadian Imperial Bank of Commerce

  2. Overview • Objective • Prediction of molecular bioactivity for drug design -- binding to Thrombin • Data • Training: 1909 cases (42 positive), 139,351 binary features • Test: 634 cases • Challenge • Highly imbalanced, high-dimensional, different distribution • My approach • Bayesian network predictive model

  3. Bayesian Networks (BN) • What is a Bayesian Network • Two ways to view it: • Encodes conditional independent relationships • Represents the joint probability distribution

  4. My work related to BN • Developed an efficient approach to learn BN from data (paper to appear in Artificial Intelligence Journal) • BN PowerConstructor system • available since 1997, thousands of downloads and many regular users • Learning BNs as predictive models • BN PowerPredictor system • available since 2000 • Applied BN learning to UCI benchmark datasets, Power plant fault diagnosis, Financial risk analysis

  5. Approach to Thrombin data • Pre-processing: Feature subset selection using mutual information (200 of 139,351 features) • Learning Bayesian network models of different complexity (2 to 12 features) • Choosing a model (ROC area, model complexity) • Cost function? • From posterior probability: only 10 cut points: 30, 31, 32, 71, 72, 74, 75, 215, 223, 550

  6. The model & its performance Activity 10695 91839 predicted 16794 79651 20 parameters Actual ROC Accuracy: 0.711Weighted Accuracy: 0.684

  7. Bayesian networks make good classifiers! • Accurate • UCI datasets • Efficient • Learning: linear to number of samples, O(N2) to the number of features – seconds to minutes • Inference: simple multiplications • Using Markov blanket for feature selection • Easy to understand, easy to incorporate domain knowledge

  8. BN PowerPredictor System • Download: http://www.cs.ualberta.ca/~jcheng/bnsoft.htm • Features: • Support domain knowledge input • Support multiple database and spreadsheet formats • Automatic feature subset selection • Automatic model selection using wrapper approach • provide equal size, equal frequency and entropy based discretization • Support cost function definition • Instant/batch inference models

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