Binary matrix factorization for analyzing gene expression data

doi:10.1007/s10618-009-0145-2

CSpace > 应用数学研究所

	Binary matrix factorization for analyzing gene expression data
	Zhang, Zhong-Yuan 2; Li, Tao 1; Ding, Chris 3; Ren, Xian-Wen 4; Zhang, Xiang-Sun4
	2010-01-10
发表期刊	DATA MINING AND KNOWLEDGE DISCOVERY
ISSN	1384-5810
卷号	20 期号:1 页码:28-52
摘要	The advent of microarray technology enables us to monitor an entire genome in a single chip using a systematic approach. Clustering, as a widely used data mining approach, has been used to discover phenotypes from the raw expression data. However traditional clustering algorithms have limitations since they can not identify the substructures of samples and features hidden behind the data. Different from clustering, biclustering is a new methodology for discovering genes that are highly related to a subset of samples. Several biclustering models/methods have been presented and used for tumor clinical diagnosis and pathological research. In this paper, we present a new biclustering model using Binary Matrix Factorization (BMF). BMF is a new variant rooted from non-negative matrix factorization (NMF). We begin by proving a new boundedness property of NMF. Two different algorithms to implement the model and their comparison are then presented. We show that the microarray data biclustering problem can be formulated as a BMF problem and can be solved effectively using our proposed algorithms. Unlike the greedy strategy-based algorithms, our proposed algorithms for BMF are more likely to find the global optima. Experimental results on synthetic and real datasets demonstrate the advantages of BMF over existing biclustering methods. Besides the attractive clustering performance, BMF can generate sparse results (i.e., the number of genes/features involved in each biclustering structure is very small related to the total number of genes/features) that are in accordance with the common practice in molecular biology.
关键词	Biclustering Non-negative matrix factorization Boundedness property of NMF Binary matrix
DOI	10.1007/s10618-009-0145-2
语种	英语
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Information Systems
WOS记录号	WOS:000273812400002
出版者	SPRINGER
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/10331
专题	应用数学研究所
通讯作者	Li, Tao
作者单位	1.Florida Int Univ, Sch Comp & Informat Sci, Miami, FL 33199 USA 2.Cent Univ Finance & Econ, Sch Stat, Beijing, Peoples R China 3.Univ Texas Arlington, Dept Comp Sci & Engn, Arlington, TX 76019 USA 4.Chinese Acad Sci, Acad Math & Syst Sci, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Zhang, Zhong-Yuan,Li, Tao,Ding, Chris,et al. Binary matrix factorization for analyzing gene expression data[J]. DATA MINING AND KNOWLEDGE DISCOVERY,2010,20(1):28-52.
APA	Zhang, Zhong-Yuan,Li, Tao,Ding, Chris,Ren, Xian-Wen,&Zhang, Xiang-Sun.(2010).Binary matrix factorization for analyzing gene expression data.DATA MINING AND KNOWLEDGE DISCOVERY,20(1),28-52.
MLA	Zhang, Zhong-Yuan,et al."Binary matrix factorization for analyzing gene expression data".DATA MINING AND KNOWLEDGE DISCOVERY 20.1(2010):28-52.