- author = "Loris Nanni and Alessandra Lumini",
- title = "A genetic approach for building different alphabets for peptide and protein classification",
- journal = "BMC Bioinformatics",
- volume = "9",
- year = "2008",
- number = "1",
- pages = "45",
- keywords = "genetic algorithms, genetic programming",
- ISSN = "1471-2105",
-
URL = "
http://www.biomedcentral.com/1471-2105/9/45",
-
DOI = "
doi:10.1186/1471-2105-9-45",
-
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.",
- notes = "\cite{Nanni201043} says its on GP",
- pubmedid = "18218100",
