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» Microarray Gene Expression Data Analysis
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CSB
2005
IEEE
205views Bioinformatics» more  CSB 2005»
15 years 3 months ago
Fractal Clustering for Microarray Data Analysis
DNA microarray experiments generate a substantial amount of information about global gene expression. Gene expression profiles can be represented as points in multi-dimensional sp...
Lu-Yong Wang, Ammaiappan Balasubramanian, Amit Cha...
BMCBI
2008
159views more  BMCBI 2008»
14 years 9 months ago
Multivariate hierarchical Bayesian model for differential gene expression analysis in microarray experiments
Background: Identification of differentially expressed genes is a typical objective when analyzing gene expression data. Recently, Bayesian hierarchical models have become increas...
Hongya Zhao, Kwok-Leung Chan, Lee-Ming Cheng, Hong...
JBI
2008
159views Bioinformatics» more  JBI 2008»
14 years 9 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
LPNMR
2005
Springer
15 years 3 months ago
Inference of Gene Relations from Microarray Data by Abduction
We describe an application of Abductive Logic Programming (ALP) to the analysis of an important class of DNA microarray experiments. We develop an ALP theory that provides a simple...
Irene Papatheodorou, Antonis C. Kakas, Marek J. Se...
BMCBI
2006
103views more  BMCBI 2006»
14 years 9 months ago
Improving missing value imputation of microarray data by using spot quality weights
Background: Microarray technology has become popular for gene expression profiling, and many analysis tools have been developed for data interpretation. Most of these tools requir...
Peter Johansson, Jari Häkkinen