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» Microarray results: how accurate are they
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BMCBI
2007
112views more  BMCBI 2007»
13 years 10 months ago
Selecting dissimilar genes for multi-class classification, an application in cancer subtyping
Background: Gene expression microarray is a powerful technology for genetic profiling diseases and their associated treatments. Such a process involves a key step of biomarker ide...
Zhipeng Cai, Randy Goebel, Mohammad R. Salavatipou...
BMCBI
2010
156views more  BMCBI 2010»
13 years 10 months ago
Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments
Background: High-throughput sequencing technologies, such as the Illumina Genome Analyzer, are powerful new tools for investigating a wide range of biological and medical question...
James H. Bullard, Elizabeth Purdom, Kasper D. Hans...
BMCBI
2004
90views more  BMCBI 2004»
13 years 9 months ago
Statistical monitoring of weak spots for improvement of normalization and ratio estimates in microarrays
Background: Several aspects of microarray data analysis are dependent on identification of genes expressed at or near the limits of detection. For example, regression-based normal...
Igor Dozmorov, Nicholas Knowlton, Yuhong Tang, Mic...
ISBRA
2007
Springer
14 years 4 months ago
Noise-Based Feature Perturbation as a Selection Method for Microarray Data
Abstract. DNA microarrays can monitor the expression levels of thousands of genes simultaneously, providing the opportunity for the identification of genes that are differentiall...
Li Chen, Dmitry B. Goldgof, Lawrence O. Hall, Stev...
BMCBI
2007
149views more  BMCBI 2007»
13 years 9 months ago
Robust imputation method for missing values in microarray data
Background: When analyzing microarray gene expression data, missing values are often encountered. Most multivariate statistical methods proposed for microarray data analysis canno...
Dankyu Yoon, Eun-Kyung Lee, Taesung Park