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» Analysis of Variance for Gene Expression Microarray Data
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BMCBI
2004
108views more  BMCBI 2004»
14 years 11 months ago
Improving the scaling normalization for high-density oligonucleotide GeneChip expression microarrays
Background: Normalization is an important step for microarray data analysis to minimize biological and technical variations. Choosing a suitable approach can be critical. The defa...
Chao Lu
CIBCB
2006
IEEE
15 years 6 months ago
A Model-Free Greedy Gene Selection for Microarray Sample Class Prediction
— Microarray data analysis is notoriously challenging as it involves a huge number of genes compared to only a limited number of samples. Gene selection, to detect the most signi...
Yi Shi, Zhipeng Cai, Lizhe Xu, Wei Ren, Randy Goeb...
KDD
2003
ACM
133views Data Mining» more  KDD 2003»
16 years 9 days ago
Interactive Analysis of Gene Interactions Using Graphical gaussian model
DNA microarray provides a powerful basis for analysis of gene expression. Data mining methods such as clustering have been widely applied to microarray data to link genes that sho...
Xintao Wu, Yong Ye, Kalpathi R. Subramanian
BMCBI
2006
126views more  BMCBI 2006»
14 years 12 months ago
Effect of data normalization on fuzzy clustering of DNA microarray data
Background: Microarray technology has made it possible to simultaneously measure the expression levels of large numbers of genes in a short time. Gene expression data is informati...
Seo Young Kim, Jae Won Lee, Jong Sung Bae
BMCBI
2007
149views more  BMCBI 2007»
15 years 18 hour ago
A unified framework for finding differentially expressed genes from microarray experiments
Background: This paper presents a unified framework for finding differentially expressed genes (DEGs) from the microarray data. The proposed framework has three interrelated modul...
Jahangheer S. Shaik, Mohammed Yeasin