Texture Classification of Proteins Using Support Vector Machines and Bio-inspired Metaheuristics

Carlos Fernandez-Lozano 1 Jose A. Seoane 2 Pablo Mesejo 3 Youssef S.G. Nashed 3 Stefano Cagnoni 3 Julian Dorado 1
1 Department of Information and Communications Technologies
2 MRC Centre for Causal Analyses in Translational Epidemiology, School of Social and Community Medicine
3 Intelligent Bio-Inspired Systems lab (IBISlab)
Abstract : In this paper, a novel classification method of two-dimensional poly-acrylamide 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 : Particle Swarm Optimization Proteomic Imaging Support Vector Machines Genetic Algorithms Texture analysis Feature Selection Electrophoresis
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Chapitre d'ouvrage
Biomedical Engineering Systems and Technologies, 452, pp.117-130, 2014, 978-3-662-44485-6. 〈10.1007/978-3-662-44485-6_9〉
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Carlos Fernandez-Lozano, Jose A. Seoane, Pablo Mesejo, Youssef S.G. Nashed, Stefano Cagnoni, et al.. Texture Classification of Proteins Using Support Vector Machines and Bio-inspired Metaheuristics. Biomedical Engineering Systems and Technologies, 452, pp.117-130, 2014, 978-3-662-44485-6. 〈10.1007/978-3-662-44485-6_9〉. 〈hal-01221496〉

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