High performance set of PseAAC and sequence based descriptors for protein classification

Loris Nanni (1) , Sheryl Brahnam , Alessandra Lumini (1)

1 Department of Electronic

The study of reliable automatic systems for protein classification is important for several domains, including finding novel drugs and vaccines. The last decade has seen a number of advances in the development of reliable systems for classifying proteins. Of particular interest has been the exploration of new methods for extracting features from a protein that enhance classification for a given problem. Most methods developed to date, however, have been evaluated in only one or two application areas. Methods have not been explored that generalize well across a number of applications areas and datasets. The aim of this study is to find a general method, or an ensemble of methods, that work well on different protein classification datasets and problems.

Mots clés: Proteins classification; Machine learning; Ensemble of classifiers; Support vector machines.; Support vector machines

Loris Nanni, Sheryl Brahnam, Alessandra Lumini. High performance set of PseAAC and sequence based descriptors for protein classification. Journal of Theoretical Biology, 2010, 266 (1), pp.1. ⟨10.1016/j.jtbi.2010.06.006⟩. ⟨hal-00613134⟩