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1	Approaches based on tree-structures classifiers to protein fold prediction Mauricio-Sanchez, David, de Andrade Lopes, Alneu, higuihara Juarez Pedro Nelson 08 1900 (has links) 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 Learning systems Protein folding Proteins Trees (mathematics) Benchmark datasets Hierarchical approach Machine learning methods Multi-class classifier Nested dichotomies Protein fold recognition Supervised methods Tree structures