# Smart Classification Paradigms for Protein Samples from 1-D Electrophoresis Gel +
## 2 State of Art in Electrophoresis and Image Retrieval  
The work of [3] works with two-dimensional electrophoresis samples and uses mass spectrography for proteoforms detection. Several spots of proteins can be found in each 2D gel. Then, the electric field is applied and the identification of proteins can be achieved. There are commercial options that perform the electrophoresis measurement. In the comparative analysis carried out by [4], the studies carried out on Total Lab 120 and Lab Image 1D are shown. These options show different functions, among them the molecular weight measurements and the improvement of the electrophoresis samples for analysis. In this study, it is possible to show that the measurements and improvements in the samples vary depending on the software used. Another system that is performed for the measurement of molecular weights also uses wavelet transform. In the work done by [5], three wavelet transforms are implemented: Daubechies level 8 with decomposition 15 (DB8-15), biorthogonal level 4.4 with decomposition 7 (BIOR4.4-7), and biorthogonal 3.3 with decomposition 4 (BIOR3.3-4). A reliability of 95% is obtained in the results. The application is in the fibromyalgia detection, providing the ability to provide medication for its treatment to those patients who require it. Another type of application for measuring molecular weights is given in the capillary analysis, where protein detection is important for patients suffering from alopecia or who may be undergoing chemotherapy treatments. The work is carried out by means of cellular analysis with mass spectrometry [6]. The work carried out by [7] exposes an analysis and improvement of images of electrophoresis samples. The research focuses on the improvement of brightness, contrast, and identification of protein spots. Even spots that are imperceptible to the human eye are detected. Only a maximum and minimum algorithm is used but a molecular weight measurement is not performed. Another way to obtain similarity in the images is that in [8] proposed an algorithm that improves distinctive characters of the image using spatial distribution, which is calculated by means of the location histogram. The location histogram provides scales and orientations of the local characteristics, in order to achieve a good recovery. Working with the analysis of images in myocardium samples provides quantitative data. Using machine vision, in this work they can classify the results and even achieve a prediction, for example, with artificial neural networks (ANN) (see [9]) and decision trees. A job where only decision trees were applied was done in [10], where they obtained a 78.39% accuracy in the classification, their objective was to use a decision tree to analyze the data and to establish a combination and to obtain a classification, the whole process started occupying 10 input variables. In [11], they made a model based on decision trees and ANN, they applied the Chi-Square Automatic Interaction Detection (CHAID) model with which they took six characteristics and obtained 74.2% effectiveness, generating four groups of classification, specifies that two of those four are main. When applying the neural network, they increased four characteristics, having 10 as the input to the ANN and obtained 75% effectiveness. A similar process is done by [12], where they trained neural networks and applied a genetic algorithm (GA) to select the weights of the networks. From the GA they selected 84 ANNs that generated 70% precision, with these ANNs they formed a multi-agent system which obtained an accuracy between 72% and 76.6%, but it is mentioned that these output results must be corroborated at a later stage. The work of models in multi objective optimization using an artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) with which they seek to predict the results, the ANN model they used was feed forward backward propagation.