# Smart Classification Paradigms for Protein Samples from 1-D Electrophoresis Gel +
Footnote †: This paper is an extended version of our paper published in MICAI 2019.  
[PERSON]  
1 Facultad de Ciencias Basicas, Ingenieria y Tecnologia, Universidad Autonoma de Tlaxcala, Apizaco 90300, Mexico; [EMAIL_ADDRESS] (J.A.F.-L.); [EMAIL_ADDRESS] (L.P.J.-Q.); [EMAIL_ADDRESS] (C.R.S.-P.)  
1 Facultad de Ciencias Basicas, Ingenieria y Tecnologia, Universidad Autonoma de Tlaxcala, Apizaco 90300, Mexico; [EMAIL_ADDRESS] (J.A.F.-L.); lidiapatricia.  
it by means of two directions: \(x\) and \(y\), likewise a \(f(x,y)\) function. Each of the pixels is the meaning of a gray level. There are several processes that can be done in an image: filtering, quality improvement, recognition, classification, segmentation, brightening, or labeling [2].  
The state of the art that has been carried out shows us only a few works that measure the molecular weights of proteins. The works in the area have focused on improving the images of the electrophoresis plates, filtering, contrast, lighting, and brightness. However, in most of them, there is no measurement by intelligent paradigms. The electrophoresis samples are taken from tissues, food, and medications, among others. On the other hand, the types of samples are of different dimensions, they can be in 1D or 2D. They have also included work that, although they do not deal with electrophoresis samples, do show some classification work with the intelligent paradigms proposed here for the measurement of molecular weights.  
The purpose of this research is to make a measurement of molecular weight in protein samples, not only quantitatively, but also visually. That is, calculating the molecular weight in kilodaltons (kD) and at the same time providing the industrial chemistry expert with other images of similar samples containing the same molecular weight by means of retrieval of visual information.  
The motivation for this work is the need for experts in industrial chemistry to perform molecular weight measurements by more accurate and economic means. These measurements are intended to be associated increasingly fast, if possible, with other similar measurements for subsequent analysis for various activities and statistics specific to the expert such as histories, visual associations, comparisons of different protein origins, and so forth.  
It is important to mention that also, within the state of the art, there is no work that makes protein measurements visually and there is rarely work on molecular weight measurements numerically speaking. The work or investigations that currently exist consist of the analysis or processing of the samples, but they do not attack or resolve the calculation or measurement of molecular weights as such.  
The hypothesis of this work is to perform a molecular weight measurement of protein profile electrophoresis samples using four different intelligent paradigms. These paradigms contemplate systems based on two different artificial neural networks, one decision tree method, and one content-based retrieval system. It is also intended that from electrophoresis images, the approximation coefficients can be obtained by means of wavelet transforms. Wavelet transforms allow obtaining parametric statistics of the samples that finally define certain ranges of values that allow mapping to molecular weights in kilodaltons.