1. Predicting Protein Interactions HERPES! Team Question Mark Jeff Brown Dante Kappotis Robert Vanderley Anthony Biasella

2. Dataset Overview The Dataset was the herpes virus. There were five different files and each one contained different attributes about the virus. All five algorithms were run on the different data sets. Domains was the largest of our data sets. It had 428 cases and 23 thousand attributes per case. The smaller of the five sets had only 44 attributes to 126 attributes. These Datasets are herpes table localization, herpes table physiochemical features, herpes table primary features, herpes table secondary features, and herpes table domains.

3. Below is a table giving the best accuracies obtained overall for each data set. ALGORITHMDOMAINS PRIMARY FEATURES SECONDARY STRUCTURE LOCALIZATION PHYSIOCHEM- ICAL DECISION TREES 70.903570.093572.196363.317866.1215 BAGGING - 73 6571 BOOSTING - 72.271.762.382 66.5888 SVM 75.934675.700973.130867.523470.0935 RANDOM FORESTS 72.429976.401976.635864.953369.8598

4. Outline of results: Overall Best Performer: SVM with an average score of 72.48% Overall Worst Performer:Boostingwith an average score of 68.15% Highest AUC ROC:Random Forests on Primary Features