_PROBLEM CoEPrA-2006_Classification_001 _GROUP_NAME Bart De Moor _GROUP_MEMBERS Olivier Gevaert Joke Allemeersch Steven Van Vooren Leo Tranchevent Nathalie Pochet Frizo Janssens Bart De Moor _ADDRESS ESAT-SCD Katholieke Universiteit Leuven Kasteelpark Arenberg 10 3001 Leuven Belgium _MODELING_PROCEDURE The data was modeled by using Bayesian networks. First a model is learned for each amino acid position separately by selecting features that correlate well with the class variable. Then these features are selected, normalised and discretised before Bayesian network learning (the selected features per amino acid are in a matlab file). Then a Bayesian network is trained for every amino acid position. Using junction tree inference we then can predict the class variable and combine the different predictions using weights (The bayesian networks are gathered in a matlab file). By using a cross validation approach both the number of features used per amino acid position and the weights for the decision integration are learned. The best number of features after 10 randomizations of the training data was 50. Moreover each position was given equal weight since different weighting schemes did not provide better results (again evaluated with 10 randomizations). Since probabilities were emitted we chose a point on the Receiver Operating Characteristic Curve (ROC) curve, equal to a maximum sum of the sensitivity and specificity. _PREDICTION Obj_00001 -1 Obj_00002 -1 Obj_00003 -1 Obj_00004 -1 Obj_00005 +1 Obj_00006 +1 Obj_00007 -1 Obj_00008 -1 Obj_00009 -1 Obj_00010 +1 Obj_00011 +1 Obj_00012 -1 Obj_00013 +1 Obj_00014 +1 Obj_00015 -1 Obj_00016 +1 Obj_00017 -1 Obj_00018 +1 Obj_00019 -1 Obj_00020 -1 Obj_00021 +1 Obj_00022 -1 Obj_00023 -1 Obj_00024 +1 Obj_00025 -1 Obj_00026 +1 Obj_00027 +1 Obj_00028 -1 Obj_00029 +1 Obj_00030 +1 Obj_00031 +1 Obj_00032 +1 Obj_00033 -1 Obj_00034 -1 Obj_00035 +1 Obj_00036 -1 Obj_00037 -1 Obj_00038 -1 Obj_00039 -1 Obj_00040 +1 Obj_00041 -1 Obj_00042 -1 Obj_00043 -1 Obj_00044 -1 Obj_00045 +1 Obj_00046 -1 Obj_00047 +1 Obj_00048 +1 Obj_00049 +1 Obj_00050 -1 Obj_00051 +1 Obj_00052 -1 Obj_00053 -1 Obj_00054 +1 Obj_00055 +1 Obj_00056 +1 Obj_00057 +1 Obj_00058 -1 Obj_00059 -1 Obj_00060 -1 Obj_00061 -1 Obj_00062 +1 Obj_00063 +1 Obj_00064 -1 Obj_00065 +1 Obj_00066 -1 Obj_00067 +1 Obj_00068 -1 Obj_00069 +1 Obj_00070 -1 Obj_00071 -1 Obj_00072 -1 Obj_00073 -1 Obj_00074 -1 Obj_00075 -1 Obj_00076 -1 Obj_00077 -1 Obj_00078 +1 Obj_00079 -1 Obj_00080 +1 Obj_00081 +1 Obj_00082 -1 Obj_00083 -1 Obj_00084 +1 Obj_00085 -1 Obj_00086 -1 Obj_00087 -1 Obj_00088 +1