Texture analysis in gel electrophoresis images using an integrative kernel-based approach

How to cite this article: Fernandez-Lozano, C. et al. Texture analysis in gel electrophoresis images using an integrative kernel-based approach. Sci. Rep. 5, 19256; doi: 10.1038/srep19256 (2015). <br>In order to generate the dataset, ten 2-DE images of different types of tissues and different experimental conditions were used. For each image out of these ten 100 regions of interest (ROI), 50 spots representing proteins and 50 representing noise (noise, background, non-protein regions) manually segmented that were selected to build a training set with 1000 samples and 274 textural features. We preprocess this dataset in order to have a standard normal distribution (a mean of zero and a standard deviation of one). The dataset is available for download at http://dx.doi.org/10.6084/m9.figshare.1368643 We calculated those features with a specialized software called Mazda. With Mazda it is only possible to define up to 16 regions of interest for each image, so there exist eight (.roi) files for each image. Please refer to Mazda user's manual for the particular instructions to load an image and (.roi) files.