| Título: | Approaches based on tree-structures classifiers to protein fold prediction |
| Autores: | Mauricio-Sanchez, David ; de Andrade Lopes, Alneu ; higuihara Juarez Pedro Nelson |
| Tipo de documento: | texto impreso |
| Editorial: | Institute of Electrical and Electronics Engineers Inc., 2018-01-16T20:34:41Z |
| Dimensiones: | application/pdf |
| Nota general: | info:eu-repo/semantics/restrictedAccess |
| Idiomas: | Inglés |
| Palabras clave: | Facultad de Ingeniería , Pregrado , Conferencias y congresos , Ingeniería de Sistemas y Computación |
| Resumen: |
El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado. Protein fold recognition is an important task in the biological area. Different machine learning methods such as multiclass classifiers, one-vs-all and ensemble nested dichotomies were applied to this task and, in most of the cases, multiclass approaches were used. In this paper, we compare classifiers organized in tree structures to classify folds. We used a benchmark dataset containing 125 features to predict folds, comparing different supervised methods and achieving 54% of accuracy. An approach related to tree-structure of classifiers obtained better results in comparison with a hierarchical approach. Revisión por pares |
| En línea: | 10.1109/INTERCON.2017.8079723 |
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