中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
LEX-SVM: EXPLORING THE POTENTIAL OF EXON EXPRESSION PROFILING FOR DISEASE CLASSIFICATION

文献类型:期刊论文

作者Yuan, Xiongying; Zhao, Yi; Liu, Changning; Bu, Dongbo
刊名JOURNAL OF BIOINFORMATICS AND COMPUTATIONAL BIOLOGY
出版日期2011-04-01
卷号9期号:2页码:299-316
关键词Alternative splicing Affymetrix exon array RNA-Seq exon expression profile classification regularized SVM
ISSN号0219-7200
DOI10.1142/S0219720011005513
英文摘要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.
WOS研究方向Biochemistry & Molecular Biology ; Computer Science ; Mathematical & Computational Biology
语种英语
WOS记录号WOS:000297077400007
出版者IMPERIAL COLLEGE PRESS
源URL[http://119.78.100.204/handle/2XEOYT63/13014]  
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.

入库方式: OAI收割

来源:计算技术研究所

浏览0
下载0
收藏0
其他版本

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。