Gene Extraction Based on Sparse Singular Value Decomposition

被引：0
|
作者：
Kong, Xiangzhen [1 ]
Liu, Jinxing [1 ,2 ]
Zheng, Chunhou [1 ]
Shang, Junliang [1 ]
机构：
[1] Qufu Normal Univ, Sch Informat Sci & Engn, Rizhao 276826, Shandong, Peoples R China
[2] Harbin Inst Technol, Shenzhen Grad Sch, Biocomp Res Ctr, Shenzhen 518055, Guangdong, Peoples R China
来源：
INTELLIGENT COMPUTING THEORIES AND APPLICATION, ICIC 2016, PT I | 2016年 / 9771卷
关键词：
Singular value decomposition; Gene extraction; Sparse constraint; Gene Ontology; COMPONENT ANALYSIS; EXPRESSION DATA; MATRIX; ENRICHMENT; LASSO; LIST; SET;
D O I：
10.1007/978-3-319-42291-6_28
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, we develop a new feature extraction method based on sparse singular value decomposition (SSVD). We apply SSVD algorithm to select the characteristic genes from Colorectal Cancer (CRC) genomic dataset, and then the differentially expressed genes obtained are evaluated by the tools based on Gene Ontology. As a gene extraction method, SSVD is also compared with some existing feature extraction methods such as independent component analysis (ICA), the p-norm robust feature extraction (PREE) and sparse principal component analysis (SPCA). The experimental results show that SSVD method outperforms the existing algorithms.
引用
收藏
页码：285 / 293
页数：9
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