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).
An Information Theoretic Approach for Improving Data Driven Prediction of Protein Model Quality / MONTUORI A; RAIMONDO G; PASERO E.G.. - In: COMPUTERS & MATHEMATICS WITH APPLICATIONS. - ISSN 0898-1221. - 55(2008), pp. 997-1006. [10.1016/j.camwa.2006.12.096]
|Titolo:||An Information Theoretic Approach for Improving Data Driven Prediction of Protein Model Quality|
|Data di pubblicazione:||2008|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.1016/j.camwa.2006.12.096|
|Appare nelle tipologie:||1.1 Articolo in rivista|