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» CoXpress: differential co-expression in gene expression data
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BMCBI
2006
123views more  BMCBI 2006»
13 years 7 months ago
Characterizing disease states from topological properties of transcriptional regulatory networks
Background: High throughput gene expression experiments yield large amounts of data that can augment our understanding of disease processes, in addition to classifying samples. He...
David Tuck, Harriet Kluger, Yuval Kluger
BMCBI
2006
147views more  BMCBI 2006»
13 years 7 months ago
Grouping Gene Ontology terms to improve the assessment of gene set enrichment in microarray data
Background: Gene Ontology (GO) terms are often used to assess the results of microarray experiments. The most common way to do this is to perform Fisher's exact tests to find...
Alex Lewin, Ian C. Grieve
BMCBI
2010
108views more  BMCBI 2010»
13 years 2 months ago
Preferred analysis methods for Affymetrix GeneChips. II. An expanded, balanced, wholly-defined spike-in dataset
Background: Concomitant with the rise in the popularity of DNA microarrays has been a surge of proposed methods for the analysis of microarray data. Fully controlled "spike-i...
Qianqian Zhu, Jeffrey C. Miecznikowski, Marc S. Ha...
JBI
2008
159views Bioinformatics» more  JBI 2008»
13 years 7 months ago
SEGS: Search for enriched gene sets in microarray data
Gene Ontology (GO) terms are often used to interpret the results of microarray experiments. The most common approach is to perform Fisher's exact tests to find gene sets anno...
Igor Trajkovski, Nada Lavrac, Jakub Tolar
BMCBI
2008
102views more  BMCBI 2008»
13 years 7 months ago
Response projected clustering for direct association with physiological and clinical response data
Background: Microarray gene expression data are often analyzed together with corresponding physiological response and clinical metadata of biological subjects, e.g. patients'...
Sung-Gon Yi, Taesung Park, Jae K. Lee