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

SUBMITTER: Fernandez-Lozano C 

PROVIDER: S-EPMC4713050 | biostudies-literature | 2016 Jan

REPOSITORIES: biostudies-literature