Visual Information retrieval is an area where the analysis and recognition is helpful to generate and obtain data from image samples. In this research the image samples are obtained from analysis of electrophoresis that captures protein profiles in tissues. The electrophoresis provides a sample where the proteins are computed to know their molecular weight. The image digital processing takes electrophoresis images and a wavelet transform is applied to the sample that can be fractioned to emphasize the molecular weights that at first sight are not identified. Therefore, with the wavelet transform, it is possible to compute molecular weights of proteins and know their corresponding weight. Electrophoresis is a technique that is used in various analysis such as DNA, medicine, environment and food. All these profiles have different molecular weights, which are known by a marker that is placed in a lane. In this proposal, the wavelet transform is applied to the images of electrophoresis samples, creating the signal of the protein with the approximation coefficients, that achieves to measure a molecular weight. The approximation coefficients are computed at 3 levels of decomposition with the wavelet transform Daubechies. It is then possible to detect a molecular weight of nearly 300 samples of electrophoresis with an accuracy of 97 % in terms of numerical and visual similarity by means of visual information retrieval evaluated with recall and precision metrics.

(2015) Cytotoxic effect of the immunotoxin constructed of the ribosome-inactivating protein curcin and the monoclonal antibody against Her2 receptor on tumor cells, Bioscience, Biotechnology, and Biochemistry, 79:6,[896][897][898][899][900][901][902][903][904][905][906]

Electrophoresis allows us to identify the types of proteins present in food, DNA, tissues and more. With the help of the molecular marker their weight is known, these markers are applied within the one-dimensional gel, and their protein value is known by means of marks. In this research, the molecular marker is obtained and the wavelet transform (WT) is obtained, generating approximation coefficients, which were taken to determine a molecular weight using three classification paradigms. The first paradigm is an approach in content-based image retrieval (CBIR) which makes a detection of the molecular weight in electrophoresis samples. The second approach is a neural network, thus two models are employed: self-organization maps (SOM) and back propagation in a supervised and unsupervised way, respectively. The third approach is based in a J48 decision tree. A comparison is made between the three paradigms for molecular weight computation. Neural networks obtained an improvement in the precision compared versus the CBIR-WT. Five parametric statistics were generated from the wavelet approximation coefficients. The CBIR-WT, SOM, back propagation and J48 were decisive for the classification and calculation of the molecular weight of each protein stain in the one-dimensional electrophoresis gel.