中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 数学与系统科学研究院
  3. 中国科学院数学与系统科学研究院
  4. 应用数学研究所
Binary matrix factorization for analyzing gene expression data

文献类型：期刊论文

作者Zhang, Zhong-Yuan2; Li, Tao1; Ding, Chris3; Ren, Xian-Wen4; Zhang, Xiang-Sun4
刊名DATA MINING AND KNOWLEDGE DISCOVERY
出版日期2010-01-10
卷号20期号:1页码:28-52
关键词Biclustering Non-negative matrix factorization Boundedness property of NMF Binary matrix
ISSN号1384-5810
DOI10.1007/s10618-009-0145-2
英文摘要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.
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000273812400002
出版者SPRINGER
源URL[http://ir.amss.ac.cn/handle/2S8OKBNM/10330]  
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.

入库方式： OAI收割

来源：数学与系统科学研究院

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。