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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

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.

Original language	English
Title of host publication	Proceedings of the 2017 IEEE 24th International Congress on Electronics, Electrical Engineering and Computing, INTERCON 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781509063628
DOIs	https://doi.org/10.1109/INTERCON.2017.8079723
State	Published - 20 Oct 2017
Externally published	Yes
Event	24th IEEE International Congress on Electronics, Electrical Engineering and Computing, INTERCON 2017 - Cusco, Peru Duration: 15 Aug 2017 → 18 Aug 2017

Publication series

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

Conference

Conference	24th IEEE International Congress on Electronics, Electrical Engineering and Computing, INTERCON 2017
Country/Territory	Peru
City	Cusco
Period	15/08/17 → 18/08/17

Access to Document

10.1109/INTERCON.2017.8079723

Cite this

Shiguihara-Juarez, P., Mauricio-Sanchez, D., & De Andrade Lopes, A. (2017). Approaches based on tree-structures classifiers to protein fold prediction. In Proceedings of the 2017 IEEE 24th International Congress on Electronics, Electrical Engineering and Computing, INTERCON 2017 Article 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.; 24th IEEE International Congress on Electronics, Electrical Engineering and Computing, INTERCON 2017 ; Conference date: 15-08-2017 Through 18-08-2017",

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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. in 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, Peru, 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).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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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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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.

T2 - 24th IEEE International Congress on Electronics, Electrical Engineering and Computing, INTERCON 2017

Y2 - 15 August 2017 through 18 August 2017

ER -

Shiguihara-Juarez P, Mauricio-Sanchez D, De Andrade Lopes A. Approaches based on tree-structures classifiers to protein fold prediction. In 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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