Kernel-based distance metric learning for microarray data classification

Background  

The most fundamental task using gene expression data in clinical oncology is to classify tissue samples according to their gene expression levels. Compared with traditional pattern classifications, gene expression-based data classification is typically characterized by high dimensionality and small sample size, which make the task quite challenging.     

Use of genomic DNA control features and predicted operon structure in microarray data analysis: ArrayLeaRNA – a Bayesian approach

Background  

Microarrays are widely used for the study of gene expression; however deciding on whether observed differences in expression are significant remains a challenge.     

Feature extraction and signal processing for nylon DNA microarrays

Background  

High-density DNA microarrays require automatic feature extraction methodologies and softwares. These can be a potential source of non-reproducibility of gene expression measurements. Variation in feature location or in signal integration methodology may be a significant contribution to the observed variance in gene expression levels.     

Comment on and reply to "Analysis of variation of amplitudes in cell cycle gene expression" by Liu,Gaido and Wolfinger: On the analysis of gene expression during the normal,eukaryotic, cell cycle

Background  

The paper of Liu, Gaido and Wolfinger on gene expression during the division cycle of HeLa cells using the data of Whitfield et al. are discussed in order to see whether their analysis is related to gene expression during the division cycle.     

Global similarity and local divergence in human and mouse gene co-expression networks

Background  

A genome-wide comparative analysis of human and mouse gene expression patterns was performed in order to evaluate the evolutionary divergence of mammalian gene expression. Tissue-specific expression profiles were analyzed for 9,105 human-mouse orthologous gene pairs across 28 tissues. Expression profiles were resolved into species-specific coexpression networks, and the topological properties of the networks were compared between species.     

Easy and rapid method for the determination of gene expression in cumulus cells incubated for oocyte maturation

Background  

The objectives of this study were to develop an easy and rapid method for measuring gene expression in a small number of cells by real-time PCR without RNA extraction and purification, and to use this method to determine more precisely IGF-I gene expression in the cumulus cells surrounding oocytes.     

Optimization of laser capture microdissection and RNA amplification for gene expression profiling of prostate cancer

Background  

To discover prostate cancer biomarkers, we profiled gene expression in benign and malignant cells laser capture microdissected (LCM) from prostate tissues and metastatic prostatic adenocarcinomas. Here we present methods developed, optimized, and validated to obtain high quality gene expression data.     

Comparative transcriptomics in <Emphasis Type="Italic">Yersinia pestis</Emphasis>: a global view of environmental modulation of gene expression

Background  

Environmental modulation of gene expression in Yersinia pestis is critical for its life style and pathogenesis. Using cDNA microarray technology, we have analyzed the global gene expression of this deadly pathogen when grown under different stress conditions in vitro.     

A consensus prognostic gene expression classifier for ER positive breast cancer

Background  

A consensus prognostic gene expression classifier is still elusive in heterogeneous diseases such as breast cancer.     

New miRNA labeling method for bead-based quantification

Background  

microRNAs (miRNAs) are small single-stranded non-coding RNAs that act as crucial regulators of gene expression. Different methods have been developed for miRNA expression profiling in order to better understand gene regulation in normal and pathological conditions. miRNAs expression values obtained from large scale methodologies such as microarrays still need a validation step with alternative technologies.     

Co-expression module analysis reveals biological processes,genomic gain,and regulatory mechanisms associated with breast cancer progression

Background  

Gene expression signatures are typically identified by correlating gene expression patterns to a disease phenotype of interest. However, individual gene-based signatures usually suffer from low reproducibility and interpretability.     

Gene knockdown by RNAi in the pea aphid <Emphasis Type="Italic">Acyrthosiphon pisum</Emphasis>

Background  

RNA interference (RNAi) is a powerful method to inhibit gene expression in a sequence specific manner.     

Diets high in selenium and isoflavones decrease androgen-regulated gene expression in healthy rat dorsolateral prostate

Background  

High dietary intake of selenium or soybean isoflavones reduces prostate cancer risk. These components each affect androgen-regulated gene expression. The objective of this work was to determine the combined effects of selenium and isoflavones on androgen-regulated gene expression in rat prostate.     

Comparison of lentiviral vector titration methods

Background  

Lentiviral vectors are efficient vehicles for stable gene transfer in dividing and non-dividing cells. Several improvements in vector design to increase biosafety and transgene expression, have led to the approval of these vectors for use in clinical studies. Methods are required to analyze the quality of lentiviral vector production, the efficiency of gene transfer and the extent of therapeutic gene expression.     

stam – a Bioconductor compliant R package for structured analysis of microarray data

Background  

Genome wide microarray studies have the potential to unveil novel disease entities. Clinically homogeneous groups of patients can have diverse gene expression profiles. The definition of novel subclasses based on gene expression is a difficult problem not addressed systematically by currently available software tools.     

Suberoylanilide hydroxamic acid induces apoptosis and sub-G1 arrest of 320 HSR colon cancer cells

Background  

Histone deacetylases and histone acetyl transferases covalently modify histone proteins, consequentially altering chromatin architecture and gene expression.     

Motif-guided sparse decomposition of gene expression data for regulatory module identification

Background  

Genes work coordinately as gene modules or gene networks. Various computational approaches have been proposed to find gene modules based on gene expression data; for example, gene clustering is a popular method for grouping genes with similar gene expression patterns. However, traditional gene clustering often yields unsatisfactory results for regulatory module identification because the resulting gene clusters are co-expressed but not necessarily co-regulated.