Abstract：
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. CONCLUSION: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.
journal_name
BMC Bioinformaticsjournal_title
BMC bioinformaticsauthors
Nanni L,Lumini Adoi
10.1186/1471-2105-9-45subject
Has Abstractpub_date
2008-01-24 00:00:00pages
45issn
1471-2105pii
1471-2105-9-45journal_volume
9pub_type
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