LEX-SVM: EXPLORING THE POTENTIAL OF EXON EXPRESSION PROFILING FOR DISEASE CLASSIFICATION

doi:10.1142/S0219720011005513

	LEX-SVM: EXPLORING THE POTENTIAL OF EXON EXPRESSION PROFILING FOR DISEASE CLASSIFICATION
	Yuan, Xiongying; Zhao, Yi; Liu, Changning; Bu, Dongbo
	2011-04-01
发表期刊	JOURNAL OF BIOINFORMATICS AND COMPUTATIONAL BIOLOGY
ISSN	0219-7200
卷号	9 期号:2 页码:299-316
摘要	Exon expression profiling technologies, including exon arrays and RNA-Seq, measure the abundance of every exon in a gene. Compared with gene expression profiling technologies like 3' array, exon expression profiling technologies could detect alterations in both transcription and alternative splicing, therefore they are expected to be more sensitive in diagnosis. However, exon expression profiling also brings higher dimension, more redundancy, and significant correlation among features. Ignoring the correlation structure among exons of a gene, a popular classification method like L1-SVM selects exons individually from each gene and thus is vulnerable to noise. To overcome this limitation, we present in this paper a new variant of SVM named Lex-SVM to incorporate correlation structure among exons and known splicing patterns to promote classification performance. Specifically, we construct a new norm, ex-norm, including our prior knowledge on exon correlation structure to regularize the coefficients of a linear SVM. Lex-SVM can be solved efficiently using standard linear programming techniques. The advantage of Lex-SVM is that it can select features group-wisely, force features in a subgroup to take equal weights and exclude the features that contradict the majority in the subgroup. Experimental results suggest that on exon expression profile, Lex-SVM is more accurate than existing methods. Lex-SVM also generates a more compact model and selects genes more consistently in cross-validation. Unlike L1-SVM selecting only one exon in a gene, Lex-SVM assigns equal weights to as many exons in a gene as possible, lending itself easier for further interpretation.
关键词	Alternative splicing Affymetrix exon array RNA-Seq exon expression profile classification regularized SVM
DOI	10.1142/S0219720011005513
收录类别	SCI
语种	英语
WOS研究方向	Biochemistry & Molecular Biology ; Computer Science ; Mathematical & Computational Biology
WOS类目	Biochemical Research Methods ; Computer Science, Interdisciplinary Applications ; Mathematical & Computational Biology
WOS记录号	WOS:000297077400007
出版者	IMPERIAL COLLEGE PRESS
引用统计	被引频次：1[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/13015
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Bu, Dongbo
作者单位	Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Yuan, Xiongying,Zhao, Yi,Liu, Changning,et al. LEX-SVM: EXPLORING THE POTENTIAL OF EXON EXPRESSION PROFILING FOR DISEASE CLASSIFICATION[J]. JOURNAL OF BIOINFORMATICS AND COMPUTATIONAL BIOLOGY,2011,9(2):299-316.
APA	Yuan, Xiongying,Zhao, Yi,Liu, Changning,&Bu, Dongbo.(2011).LEX-SVM: EXPLORING THE POTENTIAL OF EXON EXPRESSION PROFILING FOR DISEASE CLASSIFICATION.JOURNAL OF BIOINFORMATICS AND COMPUTATIONAL BIOLOGY,9(2),299-316.
MLA	Yuan, Xiongying,et al."LEX-SVM: EXPLORING THE POTENTIAL OF EXON EXPRESSION PROFILING FOR DISEASE CLASSIFICATION".JOURNAL OF BIOINFORMATICS AND COMPUTATIONAL BIOLOGY 9.2(2011):299-316.