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A New Method for Recognizing Cytokines Based on Feature Combination and a Support Vector Machine Classifier

Research on cytokine recognition is of great significance in the medical field due to the fact cytokines benefit the diagnosis and treatment of diseases, but the current methods for cytokine recognition have many shortcomings, such as low sensitivity and low F-score. Therefore, this paper proposes a...

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Detalles Bibliográficos
Autores principales: Yang, Zhe, Wang, Juan, Zheng, Zhida, Bai, Xin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6222536/
https://www.ncbi.nlm.nih.gov/pubmed/30103521
http://dx.doi.org/10.3390/molecules23082008
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author Yang, Zhe
Wang, Juan
Zheng, Zhida
Bai, Xin
author_facet Yang, Zhe
Wang, Juan
Zheng, Zhida
Bai, Xin
author_sort Yang, Zhe
collection PubMed
description Research on cytokine recognition is of great significance in the medical field due to the fact cytokines benefit the diagnosis and treatment of diseases, but the current methods for cytokine recognition have many shortcomings, such as low sensitivity and low F-score. Therefore, this paper proposes a new method on the basis of feature combination. The features are extracted from compositions of amino acids, physicochemical properties, secondary structures, and evolutionary information. The classifier used in this paper is SVM. Experiments show that our method is better than other methods in terms of accuracy, sensitivity, specificity, F-score and Matthew’s correlation coefficient.
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spelling pubmed-62225362018-11-13 A New Method for Recognizing Cytokines Based on Feature Combination and a Support Vector Machine Classifier Yang, Zhe Wang, Juan Zheng, Zhida Bai, Xin Molecules Article Research on cytokine recognition is of great significance in the medical field due to the fact cytokines benefit the diagnosis and treatment of diseases, but the current methods for cytokine recognition have many shortcomings, such as low sensitivity and low F-score. Therefore, this paper proposes a new method on the basis of feature combination. The features are extracted from compositions of amino acids, physicochemical properties, secondary structures, and evolutionary information. The classifier used in this paper is SVM. Experiments show that our method is better than other methods in terms of accuracy, sensitivity, specificity, F-score and Matthew’s correlation coefficient. MDPI 2018-08-11 /pmc/articles/PMC6222536/ /pubmed/30103521 http://dx.doi.org/10.3390/molecules23082008 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Yang, Zhe
Wang, Juan
Zheng, Zhida
Bai, Xin
A New Method for Recognizing Cytokines Based on Feature Combination and a Support Vector Machine Classifier
title A New Method for Recognizing Cytokines Based on Feature Combination and a Support Vector Machine Classifier
title_full A New Method for Recognizing Cytokines Based on Feature Combination and a Support Vector Machine Classifier
title_fullStr A New Method for Recognizing Cytokines Based on Feature Combination and a Support Vector Machine Classifier
title_full_unstemmed A New Method for Recognizing Cytokines Based on Feature Combination and a Support Vector Machine Classifier
title_short A New Method for Recognizing Cytokines Based on Feature Combination and a Support Vector Machine Classifier
title_sort new method for recognizing cytokines based on feature combination and a support vector machine classifier
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6222536/
https://www.ncbi.nlm.nih.gov/pubmed/30103521
http://dx.doi.org/10.3390/molecules23082008
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