Sentences with phrase «based gene expression analyses»

Typical microarray - based gene expression analyses compare gene expression in adjacent normal and cancerous tissues.

Not exact matches

Scientists from the Biogerontology Research Foundation (BGRF), a UK - based charity founded to support aging research and address the challenges of a rapidly aging population, propose a new concept for signalome - wide analysis of changes in intracellular pathways, called OncoFinder, which allows for accurate and robust cross-platform analysis of gene expression data.

Biomolecular model based on the gene expression data analyses support the reduction of glucose molecules (blue gradient) and acid buildup (gold gradient) proposed to occur in the boundary layer around the cell.

Indeed, the team identified high variation in adjacent «normal» tissue samples, which are typically used as control samples for comparison in analyses based on mean gene expression.

Based on analyses of over 600 drug and breast cancer cell pairings, researchers showed that, for some cells, drug exposure can cause significant changes in gene expression — indicating the successful action of a drug on its target — without affecting cell growth or survival.

In the current study, the researchers used infradian cyclic locomotor activity in the mutant mice as a proxy for mood - associated changes, and examined their molecular basis in the brain by conducting prediction analyses of the gene expression data.

Topics for the scientific sessions are: Single cell genomics and gene expression Genetic interactions RNAi and somatic cell genetics Protein - DNA interactions Cancer The meeting also highlights existing opportunities for academic and industrial researchers to access automated cell - based and biochemical technologies based at the Karolinska High Throughput Center; home to one of the most sophisticated, state - of - the - art, ultra-high performance liquid handling and analysis platforms in Europe.

Thus, interactome hubs such as NR1 may exhibit low levels of change in individual gene expression following hypoxia, but, based on analysis of interaction networks, are likely to play an important role in regulating the biologic response.

Inspired by the knowledge - based analysis methods developed for gene expression data analysis, we implemented methods for examining functionally - related SNPs as a group.

Dr Gilchrist said: «This study suggests that we may improve significantly on the widely used analysis methods for determining gene expression levels from high throughput sequence data: absolute quantitation offers a much sounder basis for determining changes in gene expression level, a measure widely used to determine the consequence of genetic, chemical or physical disturbances in living systems.»

In small cell lung cancer, starting from bioinformatics analyses of large gene expression datasets, we clustered subsets of co-expressed gene modules, derived networks of transcription factors and simulated their dynamics using logic - based mathematical modeling.

These webtools make use of the multilayered datasets from the BXD mouse population to expedite in silico gene function prediction through a series of integrative and complimentary systems analytical approaches, including (expression - based) phenome - wide association, transcriptome - / proteome - wide association, and (reverse --RRB- mediation analysis.

The webtools make use of the multilayered datasets from the BXD mouse population to expedite in silico gene function prediction through a series of integrative and complimentary systems analytical approaches, including (expression - based) phenome - wide association, transcriptome - / proteome - wide association, and (reverse --RRB- mediation analysis (Figure 1 - 2).

Because both IRF - 1 and the complex IRF - 9 / STAT2 could bind the RIG - G gene, we therefore conducted a detailed functional analysis on RIG - G promoter to precise the molecular basis for RIG - G expression.

The set of possible analyses include: 1) comparison of cell populations for the identification of differentially expressed genes; 2) dimensionality reduction for the identification of relevant coordinates; and 3) clustering of subpopulations on the base of gene expression profiles.

An approach based on testicular analysis of candidate gene expression between IVC - and in vivo (control)- produced animals was performed.

Although many common genes were expressed in the three tissues, cluster analysis based on transcript levels revealed distinct gene expression profiles of the d - 18 EET (Fig. 2C and SI Appendix, Fig.