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» NEC for Gene Expression Analysis
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JCB
2000
103views more  JCB 2000»
13 years 7 months ago
Testing for Differentially-Expressed Genes by Maximum-Likelihood Analysis of Microarray Data
Although two-color uorescent DNA microarrays are now standard equipment in many molecular biology laboratories, methods for identifying differentially expressed genes in microarra...
Trey Ideker, Vesteinn Thorsson, Andrew F. Siegel, ...
IJON
2008
128views more  IJON 2008»
13 years 7 months ago
Independent arrays or independent time courses for gene expression time series data analysis
In this paper we apply three different independent component analysis (ICA) methods, including spatial ICA (sICA), temporal ICA (tICA), and spatiotemporal ICA (stICA), to gene exp...
Sookjeong Kim, Jong Kyoung Kim, Seungjin Choi
BMCBI
2006
165views more  BMCBI 2006»
13 years 7 months ago
A stable gene selection in microarray data analysis
Background: Microarray data analysis is notorious for involving a huge number of genes compared to a relatively small number of samples. Gene selection is to detect the most signi...
Kun Yang, Zhipeng Cai, Jianzhong Li, Guohui Lin
BIBE
2003
IEEE
128views Bioinformatics» more  BIBE 2003»
14 years 26 days ago
A Repulsive Clustering Algorithm for Gene Expression Data
: - Facing the development of microarray technology, clustering is currently a leading technique to gene expression data analysis. In this paper, we propose a novel algorithm calle...
Chyun-Shin Cheng, Shiuan-Sz Wang
VLDB
2004
ACM
143views Database» more  VLDB 2004»
14 years 27 days ago
GPX: Interactive Mining of Gene Expression Data
Discovering co-expressed genes and coherent expression patterns in gene expression data is an important data analysis task in bioinformatics research and biomedical applications. ...
Daxin Jiang, Jian Pei, Aidong Zhang