Smart Classification Paradigms for Protein Samples from 1-D Electrophoresis Gel
Source abstract
Abstract
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.
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Research context
OpenAlex topics: Spectroscopy and Chemometric Analyses · Genetics, Bioinformatics, and Biomedical Research · Gene expression and cancer classification
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Facultad de Ciencias Básicas, Ingenieria y Tecnología, Universidad Autónoma de Tlaxcala, Apizaco 90300, Mexico
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