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 |
| DOI | 10.1142/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
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