Smart Classification Paradigms for Protein Samples from 1-D Electrophoresis Gel
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 molecul...
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| Format: | Article |
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MDPI AG
2020-07-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/15/5059 |
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| author | Jorge Arturo Flores-López Leticia Flores-Pulido Lidia Patricia Jaramillo-Quintero Carolina Rocío Sánchez-Pérez |
| author_facet | Jorge Arturo Flores-López Leticia Flores-Pulido Lidia Patricia Jaramillo-Quintero Carolina Rocío Sánchez-Pérez |
| author_sort | Jorge Arturo Flores-López |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T18:16:34Z |
| format | Article |
| id | doaj.art-0b9cb97a09a34c5097706804dd7beb2e |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T18:16:34Z |
| publishDate | 2020-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-0b9cb97a09a34c5097706804dd7beb2e2023-11-20T07:39:06ZengMDPI AGApplied Sciences2076-34172020-07-011015505910.3390/app10155059Smart Classification Paradigms for Protein Samples from 1-D Electrophoresis GelJorge Arturo Flores-López0Leticia Flores-Pulido1Lidia Patricia Jaramillo-Quintero2Carolina Rocío Sánchez-Pérez3Facultad de Ciencias Básicas, Ingenieria y Tecnología, Universidad Autónoma de Tlaxcala, Apizaco 90300, MexicoFacultad de Ciencias Básicas, Ingenieria y Tecnología, Universidad Autónoma de Tlaxcala, Apizaco 90300, MexicoFacultad de Ciencias Básicas, Ingenieria y Tecnología, Universidad Autónoma de Tlaxcala, Apizaco 90300, MexicoFacultad de Ciencias Básicas, Ingenieria y Tecnología, Universidad Autónoma de Tlaxcala, Apizaco 90300, MexicoElectrophoresis 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.https://www.mdpi.com/2076-3417/10/15/5059content-based image retrievalartificial neuronal networksdecision treeswavelet transformone-dimensional electrophoresis gelproteins |
| spellingShingle | Jorge Arturo Flores-López Leticia Flores-Pulido Lidia Patricia Jaramillo-Quintero Carolina Rocío Sánchez-Pérez Smart Classification Paradigms for Protein Samples from 1-D Electrophoresis Gel Applied Sciences content-based image retrieval artificial neuronal networks decision trees wavelet transform one-dimensional electrophoresis gel proteins |
| title | Smart Classification Paradigms for Protein Samples from 1-D Electrophoresis Gel |
| title_full | Smart Classification Paradigms for Protein Samples from 1-D Electrophoresis Gel |
| title_fullStr | Smart Classification Paradigms for Protein Samples from 1-D Electrophoresis Gel |
| title_full_unstemmed | Smart Classification Paradigms for Protein Samples from 1-D Electrophoresis Gel |
| title_short | Smart Classification Paradigms for Protein Samples from 1-D Electrophoresis Gel |
| title_sort | smart classification paradigms for protein samples from 1 d electrophoresis gel |
| topic | content-based image retrieval artificial neuronal networks decision trees wavelet transform one-dimensional electrophoresis gel proteins |
| url | https://www.mdpi.com/2076-3417/10/15/5059 |
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