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» Evaluation of clustering algorithms for gene expression data
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CBMS
2006
IEEE
15 years 3 months ago
Incorporating Gene Ontology in Clustering Gene Expression Data
In this paper we consider a general framework for clustering expression data that permits integration of various biological data sources through combination of corresponding dissi...
Rafal Kustra, Adam Zagdanski
BMCBI
2002
147views more  BMCBI 2002»
15 years 1 months ago
Expression profiling of human renal carcinomas with functional taxonomic analysis
Background: Molecular characterization has contributed to the understanding of the inception, progression, treatment and prognosis of cancer. Nucleic acid array-based technologies...
Michael A. Gieseg, Theresa Cody, Michael Z. Man, S...
BMCBI
2007
166views more  BMCBI 2007»
15 years 1 months ago
How to decide which are the most pertinent overly-represented features during gene set enrichment analysis
Background: The search for enriched features has become widely used to characterize a set of genes or proteins. A key aspect of this technique is its ability to identify correlati...
Roland Barriot, David J. Sherman, Isabelle Dutour
134
Voted
BMCBI
2007
149views more  BMCBI 2007»
15 years 1 months ago
A unified framework for finding differentially expressed genes from microarray experiments
Background: This paper presents a unified framework for finding differentially expressed genes (DEGs) from the microarray data. The proposed framework has three interrelated modul...
Jahangheer S. Shaik, Mohammed Yeasin
BIBE
2007
IEEE
153views Bioinformatics» more  BIBE 2007»
15 years 3 months ago
Combined expression data with missing values and gene interaction network analysis: a Markovian integrated approach
—DNA microarray technologies provide means for monitoring in the order of tens of thousands of gene expression levels quantitatively and simultaneously. However data generated in...
Juliette Blanchet, Matthieu Vignes