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© 2020. This work is licensed under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

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.

Details

Title
Smart Classification Paradigms for Protein Samples from 1-D Electrophoresis Gel
Author
Flores-López, Jorge Arturo  VIAFID ORCID Logo  ; Flores-Pulido, Leticia  VIAFID ORCID Logo  ; Jaramillo-Quintero, Lidia Patricia  VIAFID ORCID Logo  ; Carolina Rocío Sánchez-Pérez  VIAFID ORCID Logo 
First page
5059
Publication year
2020
Publication date
2020
Publisher
MDPI AG
e-ISSN
20763417
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2427811602
Copyright
© 2020. This work is licensed under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.