Approaches based on tree-structures classifiers to protein fold prediction

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...

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Main Authors:	Mauricio-Sanchez, David, de Andrade Lopes, Alneu, higuihara Juarez Pedro Nelson
Format:	Others
Language:	English
Published:	Institute of Electrical and Electronics Engineers Inc. 2018
Subjects:	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
Online Access:	http://hdl.handle.net/10757/622536

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spelling	ndltd-PERUUPC-oai-repositorioacademico.upc.edu.pe-10757-6225362020-04-09T03:19:39Z Approaches based on tree-structures classifiers to protein fold prediction Mauricio-Sanchez, David de Andrade Lopes, Alneu higuihara Juarez Pedro Nelson 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 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 2018-01-16T20:34:41Z 2018-01-16T20:34:41Z 2017-08 info:eu-repo/semantics/conferenceObject 10.1109/INTERCON.2017.8079723 http://hdl.handle.net/10757/622536 eng http://ieeexplore.ieee.org/document/8079723/ info:eu-repo/semantics/restrictedAccess application/pdf Institute of Electrical and Electronics Engineers Inc.
collection	NDLTD
language	English
format	Others
sources	NDLTD
topic	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
spellingShingle	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 Mauricio-Sanchez, David de Andrade Lopes, Alneu higuihara Juarez Pedro Nelson Approaches based on tree-structures classifiers to protein fold prediction
description	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
author	Mauricio-Sanchez, David de Andrade Lopes, Alneu higuihara Juarez Pedro Nelson
author_facet	Mauricio-Sanchez, David de Andrade Lopes, Alneu higuihara Juarez Pedro Nelson
author_sort	Mauricio-Sanchez, David
title	Approaches based on tree-structures classifiers to protein fold prediction
title_short	Approaches based on tree-structures classifiers to protein fold prediction
title_full	Approaches based on tree-structures classifiers to protein fold prediction
title_fullStr	Approaches based on tree-structures classifiers to protein fold prediction
title_full_unstemmed	Approaches based on tree-structures classifiers to protein fold prediction
title_sort	approaches based on tree-structures classifiers to protein fold prediction
publisher	Institute of Electrical and Electronics Engineers Inc.
publishDate	2018
url	http://hdl.handle.net/10757/622536
work_keys_str_mv	AT mauriciosanchezdavid approachesbasedontreestructuresclassifierstoproteinfoldprediction AT deandradelopesalneu approachesbasedontreestructuresclassifierstoproteinfoldprediction AT higuiharajuarezpedronelson approachesbasedontreestructuresclassifierstoproteinfoldprediction
_version_	1719312740057612288

Approaches based on tree-structures classifiers to protein fold prediction

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