Hierarchical Classification of Protein Folds Using a Novel Ensemble Classifier

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	Hierarchical Classification of Protein Folds Using a Novel Ensemble Classifier
	Lin, Chen ; Zou, Ying ; Qin, Ji ; Liu, Xiangrong ; Jiang, Yi ; Ke, Caihuan ; Zou, Quan ; Lin C(林琛) ; Liu XR(刘向荣) ; Jiang Y(江弋) ; Ke CH(柯才焕) ; Zou Q(邹权)
刊名	http://dx.doi.org/10.1371/journal.pone.0056499
	2013
关键词	REPRESENTATIVE SET NEURAL NETWORKS RECOGNITION SCOP SELECTION SEQUENCE PATTERNS DATABASE IMPACT MODEL
英文摘要	Natural Science Foundation of China [61001013, 61102136]; Natural Science Foundation of Fujian Province of China [2011J05158, 2010J01350]; National Basic Research Program of China [2010CB1264000]; Base Research Project of Shenzhen Bureau of Science, Technology and Information [JC201006030858A, JCYJ20120618155655087]; The analysis of biological information from protein sequences is important for the study of cellular functions and interactions, and protein fold recognition plays a key role in the prediction of protein structures. Unfortunately, the prediction of protein fold patterns is challenging due to the existence of compound protein structures. Here, we processed the latest release of the Structural Classification of Proteins (SCOP, version 1.75) database and exploited novel techniques to impressively increase the accuracy of protein fold classification. The techniques proposed in this paper include ensemble classifying and a hierarchical framework, in the first layer of which similar or redundant sequences were deleted in two manners; a set of base classifiers, fused by various selection strategies, divides the input into seven classes; in the second layer of which, an analogous ensemble method is adopted to predict all protein folds. To our knowledge, it is the first time all protein folds can be intelligently detected hierarchically. Compared with prior studies, our experimental results demonstrated the efficiency and effectiveness of our proposed method, which achieved a success rate of 74.21%, which is much higher than results obtained with previous methods (ranging from 45.6% to 70.5%). When applied to the second layer of classification, the prediction accuracy was in the range between 23.13% and 46.05%. This value, which may not be remarkably high, is scientifically admirable and encouraging as compared to the relatively low counts of proteins from most fold recognition programs. The web server Hierarchical Protein Fold Prediction (HPFP) is available at http://datamining.xmu.edu.cn/software/hpfp.
语种	英语
出版者	PUBLIC LIBRARY SCIENCE
内容类型	期刊论文
源URL	[http://dspace.xmu.edu.cn/handle/2288/92493]
专题	信息技术－已发表论文
推荐引用方式 GB/T 7714	Lin, Chen,Zou, Ying,Qin, Ji,et al. Hierarchical Classification of Protein Folds Using a Novel Ensemble Classifier[J]. http://dx.doi.org/10.1371/journal.pone.0056499,2013.
APA	Lin, Chen.,Zou, Ying.,Qin, Ji.,Liu, Xiangrong.,Jiang, Yi.,...&邹权.(2013).Hierarchical Classification of Protein Folds Using a Novel Ensemble Classifier.http://dx.doi.org/10.1371/journal.pone.0056499.
MLA	Lin, Chen,et al."Hierarchical Classification of Protein Folds Using a Novel Ensemble Classifier".http://dx.doi.org/10.1371/journal.pone.0056499 (2013).