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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
115views more  BMCBI 2010»
13 years 7 months ago
Importance of replication in analyzing time-series gene expression data: Corticosteroid dynamics and circadian patterns in rat l
Background: Microarray technology is a powerful and widely accepted experimental technique in molecular biology that allows studying genome wide transcriptional responses. However...
Tung T. Nguyen, Richard R. Almon, Debra C. DuBois,...
TCSB
2008
13 years 7 months ago
Clustering Time-Series Gene Expression Data with Unequal Time Intervals
Clustering gene expression data given in terms of time-series is a challenging problem that imposes its own particular constraints, namely exchanging two or more time points is not...
Luis Rueda, Ataul Bari, Alioune Ngom
HCI
2007
13 years 9 months ago
FPF-SB : A Scalable Algorithm for Microarray Gene Expression Data Clustering
Efficient and effective analysis of large datasets from microarray gene expression data is one of the keys to time-critical personalized medicine. The issue we address here is the ...
Filippo Geraci, Mauro Leoncini, Manuela Montangero...
BMCBI
2006
98views more  BMCBI 2006»
13 years 7 months ago
In search of functional association from time-series microarray data based on the change trend and level of gene expression
Background: The increasing availability of time-series expression data opens up new possibilities to study functional linkages of genes. Present methods used to infer functional l...
Feng He, An-Ping Zeng