A genetic approach for building different alphabets for peptide and protein classification.
BACKGROUND
In this paper, it is proposed an optimization approach for producing reduced alphabets for peptide classification, using a Genetic Algorithm. The classification task is performed by a multi-classifier system where each classifier (Linear or Radial Basis function Support Vector Machines) is trained using features extracted by different reduced alphabets. Each alphabet is constructed by a Genetic Algorithm whose objective function is the maximization of the area under the ROC-curve obtained in several classification problems.


RESULTS
The new approach has been tested in three peptide classification problems: HIV-protease, recognition of T-cell epitopes and prediction of peptides that bind human leukocyte antigens. The tests demonstrate that the idea of training a pool classifiers by reduced alphabets, created using a Genetic Algorithm, allows an improvement over other state-of-the-art feature extraction methods.


CONCLUSIONS
The validity of the novel strategy for creating reduced alphabets is demonstrated by the performance improvement obtained by the proposed approach with respect to other reduced alphabets-based methods in the tested problems.
Nanni
Loris
L
DEIS, Università di Bologna, Via Venezia 52, 47023 Cesena (FC), Italy. loris.nanni@unibo.it
Lumini
Alessandra
A
D000465:Algorithms; D000595:Amino Acid Sequence; D001185:Artificial Intelligence; D003198:Computer Simulation; D008956:Models, Chemical; D008969:Molecular Sequence Data; D010363:Pattern Recognition, Automated / Q000379:methods*; D010455:Peptides / Q000737:chemistry* / Q000145:classification; D011506:Proteins / Q000737:chemistry* / Q000145:classification; D016415:Sequence Alignment / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods*
D010455:Peptides; D011506:Proteins