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AI
2006
Springer
13 years 9 months ago
A New Profile Alignment Method for Clustering Gene Expression Data
We focus on clustering gene expression temporal profiles, and propose a novel, simple algorithm that is powerful enough to find an efficient distribution of genes over clusters. We...
Ataul Bari, Luis Rueda
BMCBI
2010
132views more  BMCBI 2010»
13 years 5 months ago
Parallel multiplicity and error discovery rate (EDR) in microarray experiments
Background: In microarray gene expression profiling experiments, differentially expressed genes (DEGs) are detected from among tens of thousands of genes on an array using statist...
Wayne Wenzhong Xu, Clay J. Carter
BIB
2007
59views more  BIB 2007»
13 years 5 months ago
Statistically designing microarrays and microarray experiments to enhance sensitivity and specificity
Gene expression signatures from microarray experiments promise to provide important prognostic tools for predicting disease outcome or response to treatment. A number of microarra...
Jason C. Hsu, Jane Chang, Tao Wang, Eiríkur...
BMCBI
2006
147views more  BMCBI 2006»
13 years 5 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
BIODATAMINING
2008
135views more  BIODATAMINING 2008»
13 years 5 months ago
Fast Gene Ontology based clustering for microarray experiments
Background: Analysis of a microarray experiment often results in a list of hundreds of diseaseassociated genes. In order to suggest common biological processes and functions for t...
Kristian Ovaska, Marko Laakso, Sampsa Hautaniemi