Approaches based on tree-structures classifiers to protein fold prediction

Pedro Shiguihara-Juarez; David Mauricio-Sanchez; Alneu De Andrade Lopes

doi:10.1109/INTERCON.2017.8079723

Approaches based on tree-structures classifiers to protein fold prediction

Pedro Shiguihara-Juarez, David Mauricio-Sanchez, Alneu De Andrade Lopes

Resultado de la investigación: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

1 Cita (Scopus)

Resumen

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.

Idioma original	Inglés
Título de la publicación alojada	Proceedings of the 2017 IEEE 24th International Congress on Electronics, Electrical Engineering and Computing, INTERCON 2017
Editorial	Institute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)	9781509063628
DOI	https://doi.org/10.1109/INTERCON.2017.8079723
Estado	Publicada - 20 oct. 2017
Publicado de forma externa	Sí
Evento	24th IEEE International Congress on Electronics, Electrical Engineering and Computing, INTERCON 2017 - Cusco, Perú Duración: 15 ago. 2017 → 18 ago. 2017

Serie de la publicación

Nombre	Proceedings of the 2017 IEEE 24th International Congress on Electronics, Electrical Engineering and Computing, INTERCON 2017

Conferencia

Conferencia	24th IEEE International Congress on Electronics, Electrical Engineering and Computing, INTERCON 2017
País/Territorio	Perú
Ciudad	Cusco
Período	15/08/17 → 18/08/17

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10.1109/INTERCON.2017.8079723

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Shiguihara-Juarez, P., Mauricio-Sanchez, D., & De Andrade Lopes, A. (2017). Approaches based on tree-structures classifiers to protein fold prediction. En Proceedings of the 2017 IEEE 24th International Congress on Electronics, Electrical Engineering and Computing, INTERCON 2017 [8079723] (Proceedings of the 2017 IEEE 24th International Congress on Electronics, Electrical Engineering and Computing, INTERCON 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/INTERCON.2017.8079723

Shiguihara-Juarez, Pedro ; Mauricio-Sanchez, David ; De Andrade Lopes, Alneu. / Approaches based on tree-structures classifiers to protein fold prediction. Proceedings of the 2017 IEEE 24th International Congress on Electronics, Electrical Engineering and Computing, INTERCON 2017. Institute of Electrical and Electronics Engineers Inc., 2017. (Proceedings of the 2017 IEEE 24th International Congress on Electronics, Electrical Engineering and Computing, INTERCON 2017).

@inproceedings{1689c36d279541e3ab2fc451c378e62b,

title = "Approaches based on tree-structures classifiers to protein fold prediction",

abstract = "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.",

author = "Pedro Shiguihara-Juarez and David Mauricio-Sanchez and {De Andrade Lopes}, Alneu",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; null ; Conference date: 15-08-2017 Through 18-08-2017",

year = "2017",

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doi = "10.1109/INTERCON.2017.8079723",

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Shiguihara-Juarez, P, Mauricio-Sanchez, D & De Andrade Lopes, A 2017, Approaches based on tree-structures classifiers to protein fold prediction. En Proceedings of the 2017 IEEE 24th International Congress on Electronics, Electrical Engineering and Computing, INTERCON 2017., 8079723, Proceedings of the 2017 IEEE 24th International Congress on Electronics, Electrical Engineering and Computing, INTERCON 2017, Institute of Electrical and Electronics Engineers Inc., 24th IEEE International Congress on Electronics, Electrical Engineering and Computing, INTERCON 2017, Cusco, Perú, 15/08/17. https://doi.org/10.1109/INTERCON.2017.8079723

Approaches based on tree-structures classifiers to protein fold prediction. / Shiguihara-Juarez, Pedro; Mauricio-Sanchez, David; De Andrade Lopes, Alneu.

Proceedings of the 2017 IEEE 24th International Congress on Electronics, Electrical Engineering and Computing, INTERCON 2017. Institute of Electrical and Electronics Engineers Inc., 2017. 8079723 (Proceedings of the 2017 IEEE 24th International Congress on Electronics, Electrical Engineering and Computing, INTERCON 2017).

Resultado de la investigación: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

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T1 - Approaches based on tree-structures classifiers to protein fold prediction

AU - Shiguihara-Juarez, Pedro

AU - Mauricio-Sanchez, David

AU - De Andrade Lopes, Alneu

PY - 2017/10/20

Y1 - 2017/10/20

N2 - 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.

AB - 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.

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U2 - 10.1109/INTERCON.2017.8079723

DO - 10.1109/INTERCON.2017.8079723

M3 - Contribución a la conferencia

AN - SCOPUS:85040015320

T3 - Proceedings of the 2017 IEEE 24th International Congress on Electronics, Electrical Engineering and Computing, INTERCON 2017

BT - Proceedings of the 2017 IEEE 24th International Congress on Electronics, Electrical Engineering and Computing, INTERCON 2017

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 15 August 2017 through 18 August 2017

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Shiguihara-Juarez P, Mauricio-Sanchez D, De Andrade Lopes A. Approaches based on tree-structures classifiers to protein fold prediction. En Proceedings of the 2017 IEEE 24th International Congress on Electronics, Electrical Engineering and Computing, INTERCON 2017. Institute of Electrical and Electronics Engineers Inc. 2017. 8079723. (Proceedings of the 2017 IEEE 24th International Congress on Electronics, Electrical Engineering and Computing, INTERCON 2017). doi: 10.1109/INTERCON.2017.8079723

Approaches based on tree-structures classifiers to protein fold prediction

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