Learning improved feature rankings through decremental input pruning for support vector based drug activity prediction

Authors:
Wladimiro Díaz-Villanueva;Francesc J. Ferri;Vicente Cerverón
Affiliations:
Departament d'Informàtica, Universitat de València, Burjassot, València, Spain;Departament d'Informàtica, Universitat de València, Burjassot, València, Spain;Departament d'Informàtica, Universitat de València, Burjassot, València, Spain
Venue:
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part II
Year:
2010

Citing 3
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Abstract

The use of certain machine learning and pattern recognition tools for automated pharmacological drug design has been recently introduced. Different families of learning algorithms and Support Vector Machines in particular have been applied to the task of associating observed chemical properties and pharmacological activities to certain kinds of representations of the candidate compounds. The purpose of this work, is to select an appropriate feature ordering from a large set of molecular descriptors usually used in the domain of Drug Activity Characterization. To this end, a new input pruning method is introduced and assessed with respect to commonly used feature ranking algorithms.