Inferring the Transcriptional Modules Using Penalized Matrix Decomposition

被引：0
|
作者：
Zheng, Chun-Hou [1 ,2 ]
Zhang, Lei [2 ]
Ng, To-Yee [2 ]
Shiu, Chi Keung [2 ]
Wang, Shu-Lin [1 ,3 ]
机构：
[1] Chinese Acad Sci, Intelligent Comp Lab, Hefei Inst Intelligent Machines, Hefei 230031, Anhui, Peoples R China
[2] Hong Kong Polytech Univ, Dept Comp, Biometr Res Ctr, Hong Kong, Hong Kong, Peoples R China
[3] Hunan Univ, Sch Comp & Commun, Changsha, Peoples R China
来源：
ADVANCED INTELLIGENT COMPUTING THEORIES AND APPLICATIONS: WITH ASPECTS OF ARTIFICIAL INTELLIGENCE | 2010年 / 6216卷
基金：
美国国家科学基金会;
关键词：
Transcriptional module; Gene expression data; Clustering; Penalized matrix decomposition; FACTORIZATION;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper proposes to use the penalized matrix decomposition (PMD) to discover the transcriptional modules from microarray data. With the sparsity constraint on the decomposition factors, metagenes can be extracted from the gene expression data and they can well capture the intrinsic patterns of genes with the similar functions. Meanwhile, the PMD factors of each gene are good indicators of the cluster it belongs to. Compared with traditional methods, our method can cluster genes of the similar functions but without similar expression profiles. It can also assign a gene into different modules.
引用
收藏
页码：35 / +
页数：2
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