title: Texture analysis in gel electrophoresis images using an integrative kernel-based approach.
abstract: Texture information could be used in proteomics to improve the quality of the image analysis of proteins separated on a gel. In order to evaluate the best technique to identify relevant textures, we use several different kernel-based machine learning techniques to classify proteins in 2-DE images into spot and noise. We evaluate the classification accuracy of each of these techniques with proteins extracted from ten 2-DE images of different types of tissues and different experimental conditions. We found that the best classification model was FSMKL, a data integration method using multiple kernel learning, which achieved AUROC values above 95% while using a reduced number of features. This technique allows us to increment the interpretability of the complex combinations of textures and to weight the importance of each particular feature in the final model. In particular the Inverse Difference Moment exhibited the highest discriminating power. A higher value can be associated with an homogeneous structure as this feature describes the homogeneity; the larger the value, the more symmetric. The final model is performed by the combination of different groups of textural features. Here we demonstrated the feasibility of combining different groups of textures in 2-DE image analysis for spot detection.
authors.affiliation: Information and Communication Technologies Department, Faculty of Computer Science, University of A Coruna, A Coruna, 15071, Spain.
authors.forename: Carlos
authors.initials: C
authors.lastname: Fernandez-Lozano
authors.affiliation: Bristol Genetic Epidemiology Laboratories, School of Social and Community Medicine, University of Bristol, Bristol BS82BN, UK.|Stanford Cancer Institute, Stanford School of Medicine, Stanford University, Stanford, 94305, USA.
authors.forename: Jose A
authors.initials: JA
authors.lastname: Seoane
authors.affiliation: Information and Communication Technologies Department, Faculty of Computer Science, University of A Coruna, A Coruna, 15071, Spain.
authors.forename: Marcos
authors.initials: M
authors.lastname: Gestal
authors.affiliation: MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Bristol BS82BN, UK.
authors.forename: Tom R
authors.initials: TR
authors.lastname: Gaunt
authors.affiliation: Information and Communication Technologies Department, Faculty of Computer Science, University of A Coruna, A Coruna, 15071, Spain.
authors.forename: Julian
authors.initials: J
authors.lastname: Dorado
authors.affiliation: Information and Communication Technologies Department, Faculty of Computer Science, University of A Coruna, A Coruna, 15071, Spain.|Instituto de Investigacion Biomedica de A Coruña (INIBIC), Complexo Hospitalario Universitario de A Coruña (CHUAC), A Coruña, 15006, Spain.
authors.forename: Alejandro
authors.initials: A
authors.lastname: Pazos
authors.affiliation: Intelligent Systems Laboratory, University of Bristol, Bristol BS81UB, UK.
authors.forename: Colin
authors.initials: C
authors.lastname: Campbell
mesh_terms: D004586:Electrophoresis; D007091:Image Processing, Computer-Assisted; D000069550:Machine Learning; D015203:Reproducibility of Results