Predicción estructural de proteínas usando técnicas de clasificación
In this paper, a new protein structure prediction method is presented. Unlike current methods, this work introduces an approach based on supervised classification algorithms during the protein structure prediction. The accuracy of the proposed method was compared to traditional methods such as LFF (...
- Autores:
- Tipo de recurso:
- Fecha de publicación:
- 2018
- Institución:
- Universidad Industrial de Santander
- Repositorio:
- Repositorio UIS
- Idioma:
- spa
- OAI Identifier:
- oai:noesis.uis.edu.co:20.500.14071/8365
- Acceso en línea:
- https://revistas.uis.edu.co/index.php/revistauisingenierias/article/view/6409
https://noesis.uis.edu.co/handle/20.500.14071/8365
- Palabra clave:
- Bioinformatics
classifiers
structural prediction
proteins
SCOP
Bioinformática
clasificadores
predicción estructural
proteínas
SCOP
- Rights
- openAccess
- License
- Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
| Summary: | In this paper, a new protein structure prediction method is presented. Unlike current methods, this work introduces an approach based on supervised classification algorithms during the protein structure prediction. The accuracy of the proposed method was compared to traditional methods such as LFF (Local Feature Frequency) when using the Scop 2,05 dataset. The results indicate that there is a significant difference between these two methods. The proposed method reaches accuracy values of 92.13 %, 96.32 %, 93.05 %, and 76.35 %, at class, fold, superfamily, and family levels, respectively, and the LFF method reaches accuracy values of 85.90 %, 90.54 %, 79.85 % and 67.38 %, for the same structural levels. |
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