An information theoretic approach for improving data driven prediction of protein model quality

Authors:
Alfonso Montuori;Giovanni Raimondo;Eros Pasero
Affiliations:
Department of Electronics, Politecnico di Torino, 10139 Torino, Italy;Department of Electronics, Politecnico di Torino, 10139 Torino, Italy;Department of Electronics, Politecnico di Torino, 10139 Torino, Italy
Venue:
Computers & Mathematics with Applications
Year:
2008

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Abstract

We present the results of an information theory-based approach to select an optimal subset of features for the prediction of protein model quality. The optimal subset of features was calculated by means of a backward selection procedure. The performances of a probabilistic classifier modeled by means of a Kernel Probability Density Estimation method (KPDE) were compared with those of a feed-forward Artificial Neural Network (ANN) and a Support Vector Machine (SVM).