Authors:
Carlos Fernandez-Lozano
1
;
Jose A. Seoane
1
;
Pablo Mesejo
2
;
Youssef S. G. Nashed
2
;
Stefano Cagnoni
2
and
Julian Dorado
1
Affiliations:
1
University of A Coruña, Spain
;
2
University of Parma, Italy
Keyword(s):
Texture Analysis, Feature Selection, Electrophoresis, Support Vector Machines, Genetic Algorithm.
Related
Ontology
Subjects/Areas/Topics:
Algorithms and Software Tools
;
Bioinformatics
;
Biomedical Engineering
;
Data Mining and Machine Learning
;
Genomics and Proteomics
;
Image Analysis
;
Pattern Recognition, Clustering and Classification
Abstract:
In this paper, a novel texture classification method from two-dimensional electrophoresis gel images is presented. Such a method makes use of textural features that are reduced to a more compact and efficient subset of characteristics by means of a Genetic Algorithm-based feature selection technique. Then, the selected features are used as inputs for a classifier, in this case a Support Vector Machine. The accuracy of the proposed method is around 94%, and has shown to yield statistically better performances than the classification based on the entire feature set. We found that the most decisive and representative features for the textural classification of proteins are those related to the second order co-occurrence matrix. This classification step can be very useful in order to discard over-segmented areas after a protein segmentation or identification process.