Motivation: Standard analysis routines for microarray data aim at differentially expressed genes. In this paper, we address the complementary problem of detecting sets of differentially co-expressed genes in two phenotypically distinct sets of expression profiles. Results: We introduce a score for differential co-expression and suggest a computationally efficient algorithm for finding high scoring sets of genes. The use of our novel method is demonstrated in the context of simulations and on real expression data from a clinical study
The problem of identifying significantly differentially expressed genes for replicated microarray ex...
This paper presents a unified framework for finding differentially expressed genes (DEGs) from the t...
This paper presents a unified framework for finding differentially expressed genes (DEGs) from the t...
Motivation: Standard analysis routines for microarray data aim at differentially expressed genes. I...
MOTIVATION: Standard analysis routines for microarray data aim at differentially expressed genes. In...
Motivation: The power of a microarray experiment derives from the identification of genes differenti...
for Identifying Differential Co-expression in High-throughput Experiments A common goal of microarra...
Motivation: Alteration of gene expression often results in up- or down-regulated genes and the most ...
The power of a microarray experiment derives from the identification of genes differentially regulat...
<div><p>Comparing the gene-expression profiles of sick and healthy individuals can help in understan...
With the development of DNA microarray technology, scientists can now measure the expression levels ...
With the development of DNA microarray technology, scientists can now measure the expression levels ...
AbstractDifferential gene expression analysis between healthy and diseased groups is a widely used a...
Motivation: Microarray experiments typically analyze thousands to tens of thousands of genes from sm...
Microarrays are a powerful tool for studying differential gene expression. However, lists of many di...
The problem of identifying significantly differentially expressed genes for replicated microarray ex...
This paper presents a unified framework for finding differentially expressed genes (DEGs) from the t...
This paper presents a unified framework for finding differentially expressed genes (DEGs) from the t...
Motivation: Standard analysis routines for microarray data aim at differentially expressed genes. I...
MOTIVATION: Standard analysis routines for microarray data aim at differentially expressed genes. In...
Motivation: The power of a microarray experiment derives from the identification of genes differenti...
for Identifying Differential Co-expression in High-throughput Experiments A common goal of microarra...
Motivation: Alteration of gene expression often results in up- or down-regulated genes and the most ...
The power of a microarray experiment derives from the identification of genes differentially regulat...
<div><p>Comparing the gene-expression profiles of sick and healthy individuals can help in understan...
With the development of DNA microarray technology, scientists can now measure the expression levels ...
With the development of DNA microarray technology, scientists can now measure the expression levels ...
AbstractDifferential gene expression analysis between healthy and diseased groups is a widely used a...
Motivation: Microarray experiments typically analyze thousands to tens of thousands of genes from sm...
Microarrays are a powerful tool for studying differential gene expression. However, lists of many di...
The problem of identifying significantly differentially expressed genes for replicated microarray ex...
This paper presents a unified framework for finding differentially expressed genes (DEGs) from the t...
This paper presents a unified framework for finding differentially expressed genes (DEGs) from the t...