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LecR1-antibodies - Learning from Diversity Epitope...

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Learning from Diversity Epitope Prediction with Sequence and Structure Features using an Ensemble of Support Vector Machines Rob Patro and Carl Kingsford Center for Bioinformatics and Computational Biology University of Maryland Nov. 16, 2010 N
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Overview Challenge: epitope-antibody recognition Solution: ensemble of support vector machines I Trained with probabilistic extension I Variety of feature classes : physicochemical properties, string kernels, structure I Performance of individual methods and ensemble N
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Problem Overview The Challenge ©http://visualscience.ru, 2010 } Binding Site { Binding Site ? Binding with linear epitopes “Simpler” sequence affinity relation The Details Measure binding affinity aff ( p i ) [ 0 , 65536 ] C + = { p i | aff ( p i ) [ 10000 , 65536 ] } 6 , 841 binders C - = { p i | aff ( p i ) [ 0 , 1000 ] } 20 , 437 non-binders Learn a function to predict binding f : P → [ 0 , 1 ] f ( p i ) 0 . 5 = p ∈ C + f ( p i ) < 0 . 5 = p ∈ C - N
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System Overview ? C - f 0 f 1 . . . f M 0 0.5 1.0 } } C + Individual classifiers trained on various features Decision Trees, Boosted / Bagged / Random Forests, Naive Bayes, Logistic Regression, Maximum Entropy Classification, (Balanced) Winnow Classifiers, etc. Support Vector Machines (SVM) Aggregate scores of classifiers Produces prediction for binding class Unlikely Binder Likely Binder N
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Probabilistic SVMs Ideally we want a confidence in each prediction ( Platt:1999 ) For each prediction, we obtain a posterior probability Allows ranking of predictions by posterior Aids in classifier combination C - 0 0.5 1.0 } } C + N
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Combining Predictions
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