@inproceedings{fcc21b90e0134e6381f0cb4aadafdbe3,
title = "Texture classification of proteins using support vector machines and bio-inspired metaheuristics",
abstract = "In this paper, a novel classification method of two-dimensional polyacrylamide gel electrophoresis images is presented. Such a method uses textural features obtained by means of a feature selection process for whose implementation we compare Genetic Algorithms and Particle Swarm Optimization. Then, the selected features, among which the most decisive and representative ones appear to be those related to the second order co-occurrence matrix, are used as inputs for a Support Vector Machine. The accuracy of the proposed method is around 94 %, a statistically better performance than the classification based on the entire feature set. This classification step can be very useful for discarding over-segmented areas after a protein segmentation or identification process.",
keywords = "Electrophoresis, Feature Selection, Genetic Algorithm, Proteomic Imaging, Support, Texture Analysis, Vector Machines",
author = "Carlos Fernandez-Lozano and Julian Dorado and Seoane, {Jose A.} and Pablo Mesejo and Nashed, {Youssef S.G.} and Stefano Cagnoni",
note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 2014.; 6th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2013 ; Conference date: 11-02-2013 Through 14-02-2013",
year = "2014",
doi = "10.1007/978-3-662-44485-6_9",
language = "English (US)",
series = "Communications in Computer and Information Science",
publisher = "Springer Verlag",
pages = "117--130",
editor = "Ana Fred and Hugo Gamboa and Fernandes, {Pedro L.} and Jordi Sol{\'e}-Casals and Mireya Fern{\'a}ndez-Chimeno and Sergio Alvarez and Deborah Stacey",
booktitle = "Biomedical Engineering Systems and Technologies - 6th International Joint Conference, BIOSTEC 2013, Revised Selected Papers",
}