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 (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.

• 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)