Protein Fold Recognition with Adaptive Local Hyperplane Algorithm

Kecman, V; Yang, T

Protein Fold Recognition with Adaptive Local Hyperplane Algorithm

Kecman, V Yang, T

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (IEEE CIBCB), 2009, pp. 1 - 4
Issue Date:: 2009-01

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Field	Value	Language
dc.contributor.author	Kecman, V	en_US
dc.contributor.author	Yang, T	en_US
dc.contributor.editor	Vincenzo Piuri	en_US
dc.date	2009-03-30	en_US
dc.date.issued	2009-01	en_US
dc.identifier.citation	IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (IEEE CIBCB), 2009, pp. 1 - 4	en_US
dc.identifier.isbn	978-1-4244-2756-7	en_US
dc.identifier.uri	http://hdl.handle.net/10453/16374
dc.description.abstract	Protein fold recognition task is important for understanding the biological functions of proteins. The adaptive local hyperplane (ALH) algorithm has been shown to perform better than many other renown classifiers including support vector machines, K-nearest neighbor, linear discriminant analysis, K-local hyperplane distance nearest neighbor algorithms and decision trees on a variety of data sets. In this paper, we apply the ALH algorithm to well-known data sets on protein fold recognition task without sequence similarity from Ding and Dubchak (2001). The results obtained demonstrate that the ALH algorithm outperforms all the seven other very well known and established benchmarking classifiers applied to same data sets.	en_US
dc.publisher	IEEE	en_US
dc.relation.ispartof	IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (IEEE CIBCB)	en_US
dc.relation.ispartof	IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology	en_US
dc.relation.isbasedon	10.1109/CIBCB.2009.4925710	en_US
dc.rights	© 2009 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.title	Protein Fold Recognition with Adaptive Local Hyperplane Algorithm	en_US
dc.type	Conference Proceeding
utslib.location	USA	en_US
utslib.location.activity	Nashville, USA	en_US
utslib.for	1107 Immunology	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Nashville, USA	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access
pubs.consider-herdc	false	en_US
pubs.place-of-publication	USA	en_US
pubs.start-date	2009-03-30	en_US

Abstract:

Protein fold recognition task is important for understanding the biological functions of proteins. The adaptive local hyperplane (ALH) algorithm has been shown to perform better than many other renown classifiers including support vector machines, K-nearest neighbor, linear discriminant analysis, K-local hyperplane distance nearest neighbor algorithms and decision trees on a variety of data sets. In this paper, we apply the ALH algorithm to well-known data sets on protein fold recognition task without sequence similarity from Ding and Dubchak (2001). The results obtained demonstrate that the ALH algorithm outperforms all the seven other very well known and established benchmarking classifiers applied to same data sets.

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http://hdl.handle.net/10453/16374