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Prediction of protein structural class with Rough Sets

BACKGROUND: A new method for the prediction of protein structural classes is constructed based on Rough Sets algorithm, which is a rule-based data mining method. Amino acid compositions and 8 physicochemical properties data are used as conditional attributes for the construction of decision system....

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Detalles Bibliográficos
Autores principales: Cao, Youfang, Liu, Shi, Zhang, Lida, Qin, Jie, Wang, Jiang, Tang, Kexuan
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2006
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1363362/
https://www.ncbi.nlm.nih.gov/pubmed/16412240
http://dx.doi.org/10.1186/1471-2105-7-20
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author Cao, Youfang
Liu, Shi
Zhang, Lida
Qin, Jie
Wang, Jiang
Tang, Kexuan
author_facet Cao, Youfang
Liu, Shi
Zhang, Lida
Qin, Jie
Wang, Jiang
Tang, Kexuan
author_sort Cao, Youfang
collection PubMed
description BACKGROUND: A new method for the prediction of protein structural classes is constructed based on Rough Sets algorithm, which is a rule-based data mining method. Amino acid compositions and 8 physicochemical properties data are used as conditional attributes for the construction of decision system. After reducing the decision system, decision rules are generated, which can be used to classify new objects. RESULTS: In this study, self-consistency and jackknife tests on the datasets constructed by G.P. Zhou (Journal of Protein Chemistry, 1998, 17: 729–738) are used to verify the performance of this method, and are compared with some of prior works. The results showed that the rough sets approach is very promising and may play a complementary role to the existing powerful approaches, such as the component-coupled, neural network, SVM, and LogitBoost approaches. CONCLUSION: The results with high success rates indicate that the rough sets approach as proposed in this paper might hold a high potential to become a useful tool in bioinformatics.
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spelling pubmed-13633622006-02-10 Prediction of protein structural class with Rough Sets Cao, Youfang Liu, Shi Zhang, Lida Qin, Jie Wang, Jiang Tang, Kexuan BMC Bioinformatics Research Article BACKGROUND: A new method for the prediction of protein structural classes is constructed based on Rough Sets algorithm, which is a rule-based data mining method. Amino acid compositions and 8 physicochemical properties data are used as conditional attributes for the construction of decision system. After reducing the decision system, decision rules are generated, which can be used to classify new objects. RESULTS: In this study, self-consistency and jackknife tests on the datasets constructed by G.P. Zhou (Journal of Protein Chemistry, 1998, 17: 729–738) are used to verify the performance of this method, and are compared with some of prior works. The results showed that the rough sets approach is very promising and may play a complementary role to the existing powerful approaches, such as the component-coupled, neural network, SVM, and LogitBoost approaches. CONCLUSION: The results with high success rates indicate that the rough sets approach as proposed in this paper might hold a high potential to become a useful tool in bioinformatics. BioMed Central 2006-01-14 /pmc/articles/PMC1363362/ /pubmed/16412240 http://dx.doi.org/10.1186/1471-2105-7-20 Text en Copyright © 2006 Cao et al; licensee BioMed Central Ltd.
spellingShingle Research Article
Cao, Youfang
Liu, Shi
Zhang, Lida
Qin, Jie
Wang, Jiang
Tang, Kexuan
Prediction of protein structural class with Rough Sets
title Prediction of protein structural class with Rough Sets
title_full Prediction of protein structural class with Rough Sets
title_fullStr Prediction of protein structural class with Rough Sets
title_full_unstemmed Prediction of protein structural class with Rough Sets
title_short Prediction of protein structural class with Rough Sets
title_sort prediction of protein structural class with rough sets
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1363362/
https://www.ncbi.nlm.nih.gov/pubmed/16412240
http://dx.doi.org/10.1186/1471-2105-7-20
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