.: Marcos Gestal :.     -----


Datos de la Publicación:

Carlos Fernandez-Lozano, José Antonio Seoane, Marcos Gestal, Tom R. Gaunt, Julián Dorado, Alejandro Pazos, Colin Campbell
Título: Texture analysis in gel electrophoresis images using an integrative kernel-based approach
Revista: Scientific Reports
Páginas: 1-13
Editorial: Nature Publishing Group
Fecha Publicación: Enero 2016
Factor de Impacto: 5.578
doi: http://dx.doi.org/10.1038/srep19256
Categorías WoS: Multidisciplinary Sciences - Cuartil: Q1 - Tercil: T1 - Posición 5 de 57


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.