Finding groups in gene expression data

被引:20
作者
Hand, DJ [1 ]
Heard, NA [1 ]
机构
[1] Univ London Imperial Coll Sci Technol & Med, Fac Phys Sci, Dept Math, London SW7 2AZ, England
来源
JOURNAL OF BIOMEDICINE AND BIOTECHNOLOGY | 2005年 / 02期
关键词
D O I
10.1155/JBB.2005.215
中图分类号
Q81 [生物工程学(生物技术)]; Q93 [微生物学];
学科分类号
071005 ; 0836 ; 090102 ; 100705 ;
摘要
The vast potential of the genomic insight offered by microarray technologies has led to their widespread use since they were introduced a decade ago. Application areas include gene function discovery, disease diagnosis, and inferring regulatory networks. Microarray experiments enable large-scale, high-throughput investigations of gene activity and have thus provided the data analyst with a distinctive, high-dimensional field of study. Many questions in this field relate to finding subgroups of data profiles which are very similar. A popular type of exploratory tool for finding subgroups is cluster analysis, and many different flavors of algorithms have been used and indeed tailored for microarray data. Cluster analysis, however, implies a partitioning of the entire data set, and this does not always match the objective. Sometimes pattern discovery or bump hunting tools are more appropriate. This paper reviews these various tools for finding interesting subgroups.
引用
收藏
页码:215 / 225
页数:11
相关论文
共 50 条
[41]   Finding semantic associations in hierarchically structured groups of Web data [J].
Rosaci, Domenico .
FORMAL ASPECTS OF COMPUTING, 2015, 27 (5-6) :867-884
[42]   Finding Correlated Bicluster from Gene Expression Data of Alzheimer Disease Using FABIA Biclustering Method [J].
Setyaningrum, Nuning ;
Bustamam, Alhadi ;
Siswantining, Titin .
PROCEEDINGS OF THE SYMPOSIUM ON BIOMATHEMATICS (SYMOMATH) 2018, 2019, 2084
[43]   Finding gene-expression patterns in bacterial biofilms [J].
Beloin, C ;
Ghigo, JM .
TRENDS IN MICROBIOLOGY, 2005, 13 (01) :16-19
[44]   Finding transcriptional regulatory elements in Dictyostelium gene expression [J].
Seo, D ;
Yasunaga, M ;
Kim, IS ;
Ham, BW ;
Kim, JH .
2005 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-3, PROCEEDINGS, 2005, :1746-1752
[45]   An improved probabilistic model for finding differential gene expression [J].
Zhang, Li ;
Liu, Xuejun .
PROCEEDINGS OF THE 2009 2ND INTERNATIONAL CONFERENCE ON BIOMEDICAL ENGINEERING AND INFORMATICS, VOLS 1-4, 2009, :1566-1571
[46]   Finding Temporal Gene Expression Patterns for Translational Research [J].
Tusch, Guenter ;
Tole, Olvi ;
Kutsumi, Yuka ;
Sam, Vincent K. ;
Mamidi, Lakshmi .
MEDINFO 2013: PROCEEDINGS OF THE 14TH WORLD CONGRESS ON MEDICAL AND HEALTH INFORMATICS, PTS 1 AND 2, 2013, 192 :1173-1173
[47]   geneRFinder: gene finding in distinct metagenomic data complexities [J].
Raíssa Silva ;
Kleber Padovani ;
Fabiana Góes ;
Ronnie Alves .
BMC Bioinformatics, 22
[48]   geneRFinder: gene finding in distinct metagenomic data complexities [J].
Silva, Raissa ;
Padovani, Kleber ;
Goes, Fabiana ;
Alves, Ronnie .
BMC BIOINFORMATICS, 2021, 22 (01)
[49]   Finding the optimal gene order in displaying microarray data [J].
Lee, SK ;
Kim, YH ;
Moon, BR .
GENETIC AND EVOLUTIONARY COMPUTATION - GECCO 2003, PT II, PROCEEDINGS, 2003, 2724 :2215-2226
[50]   T-profiler: scoring the activity of predefined groups of genes using gene expression data [J].
Boorsma, A ;
Foat, BC ;
Vis, D ;
Klis, F ;
Bussemaker, HJ .
NUCLEIC ACIDS RESEARCH, 2005, 33 :W592-W595