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» Analysis of variance components in gene expression data
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BMCBI
2007
149views more  BMCBI 2007»
14 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
BMCBI
2010
108views more  BMCBI 2010»
14 years 10 months ago
Comparison of scores for bimodality of gene expression distributions and genome-wide evaluation of the prognostic relevance of h
Background: A major goal of the analysis of high-dimensional RNA expression data from tumor tissue is to identify prognostic signatures for discriminating patient subgroups. For t...
Birte Hellwig, Jan G. Hengstler, Marcus Schmidt, M...
WILF
2005
Springer
112views Fuzzy Logic» more  WILF 2005»
15 years 3 months ago
NEC for Gene Expression Analysis
Aim of this work is to apply a novel comprehensive machine learning tool for data mining to preprocessing and interpretation of gene expression data. Furthermore, some visualizatio...
Roberto Amato, Angelo Ciaramella, N. Deniskina, Ca...
BMCBI
2007
133views more  BMCBI 2007»
14 years 9 months ago
Semi-supervised learning for the identification of syn-expressed genes from fused microarray and in situ image data
Background: Gene expression measurements during the development of the fly Drosophila melanogaster are routinely used to find functional modules of temporally co-expressed genes. ...
Ivan G. Costa, Roland Krause, Lennart Opitz, Alexa...
BMCBI
2005
212views more  BMCBI 2005»
14 years 9 months ago
PAGE: Parametric Analysis of Gene Set Enrichment
Background: Gene set enrichment analysis (GSEA) is a microarray data analysis method that uses predefined gene sets and ranks of genes to identify significant biological changes i...
Seon-Young Kim, David J. Volsky