Sentences with phrase «gene expression data in»

deCODE will apply its expertise in in vitro pharmacogenomics to provide gene expression data in relation to a Wyeth candidate treatment for respiratory disease Reykjavik, ICELAND, November 20, 2002 — deCODE genetics (Nasdaq / Nasdaq Europe: DCGN) today announced the...

deCODE will apply its expertise in in vitro pharmacogenomics to provide gene expression data in relation to a Wyeth candidate treatment for respiratory disease

The aim of his research was to use microarray gene expression data in order to characterize ageing in different human tissues and identify the age at which major changes in genetic expression profiles occur.

In the Nature Methods paper, Corrada Bravo, UMD computer science doctoral student Florin Chelaru, and undergraduate research assistants from Williams College in Mass. and Washington University in St. Louis used Epiviz to visualize and analyze DNA methylation and gene expression data in colon cancer.

«For several years the potential for the use of gene expression data in research and clinical applications has been underappreciated due to the inconsistency of the data coming from the various types of equipment.

Not exact matches

The challenge is substantial — the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus repository (GEO) alone contains 80,985 public datasets, spanning hundreds of tissue types in thousands of organisms — and the rapid growth in data makes it difficult for journals or data repositories to «police» whether datasets that should be made publicly available actually are.

«It is exciting to find a correlation between brain circuitry and gene expression by combining high quality data from these two large - scale projects,» says David Van Essen, Ph.D., professor at Washington University in St. Louis and a leader of the Human Connectome Project.

In contrast, when the OncoFinder algorithm is applied to the data, a clear correlation between next generation sequencing and microarray gene expression datasets was seen.

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.

In this study, a team led by Panos N. Papapanou, DDS, PhD, professor and chair of oral, diagnostic and rehabilitation sciences at the College of Dental Medicine at CUMC, «reverse - engineered» the gene expression data to build a map of the genetic interactions that lead to periodontitis and identify individual genes that appear to have the most influence on the disease.

Our data indicate that gene expression is coordinately regulated, such that states of increased proliferation are associated with widespread reductions in the 3 ′ UTR - based regulatory capacity of mRNAs.

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.

Using breast cancer patient data taken from The Cancer Genome Atlas, molecular biologists Curt M. Horvath and Robert W. Tell used powerful computational and bioinformatics techniques to detect patterns of gene expression in two cancer subtypes.

This new gene expression data therefore provides additional evidence that the altered behavior of bacteria in space results from decreased gravity driving reduced extracellular transport of molecules.

Epiviz implements multiple visualization methods for location - based data (such as genomic regions of interest) and feature - based data (such as gene expression), using interactive data visualization techniques not available in web - based genome browsers.

Using clinical, genetic, and gene expression data as filters to distinguish genes whose copy number alteration causes cancer from those for whom copy number changes are incidental, the team whittled down their list from 14,000 to a more manageable number, each of which they systematically tested using genetic experiments in animals.

The researchers developed algorithms to use in a «systems biology modeling cycle,» in which they repeatedly fit a model to gene expression data obtained from laboratory experiments until a good fit was obtained between the predicted and the measured outcomes.

Monitoring these expression ratios in live cells would provide prospective gene expression data as opposed to traditional retrospective characterization.

They tested this theory in mice, rats, flies and fish using publicly available gene - expression data.

One important level of information discovered in their data was a record of past gene expression.

MEGENA (for Multiscale Embedded Gene Co-expression Network Analysis) projects gene expression data onto a three dimensional sphere, allowing scientists to study hierarchical organization patterns in complex networks that are characteristic of diseases such as cancer, obesity, and AlzheimeGene Co-expression Network Analysis) projects gene expression data onto a three dimensional sphere, allowing scientists to study hierarchical organization patterns in complex networks that are characteristic of diseases such as cancer, obesity, and Alzheimegene expression data onto a three dimensional sphere, allowing scientists to study hierarchical organization patterns in complex networks that are characteristic of diseases such as cancer, obesity, and Alzheimer's.

In a Cell paper published on April 7, Lanner's team analysed gene expression in 88 early human embryos and is using those data to identify genes to disrupt in embryos using CRISPR — CasIn a Cell paper published on April 7, Lanner's team analysed gene expression in 88 early human embryos and is using those data to identify genes to disrupt in embryos using CRISPR — Casin 88 early human embryos and is using those data to identify genes to disrupt in embryos using CRISPR — Casin embryos using CRISPR — Cas9.

These data suggest that PAD4 mediates gene expression by regulating Arg methylation and citrullination in histones.

The accumulation of adipose tissue macrophages in direct proportion to adipocyte size and body mass may explain the coordinated increase in expression of genes encoding macrophage markers observed in our microarray expression data.

The expression of mRNA for factors involved in promoting mitochondrial biogenesis, including the transcription factor Ppard, the PPARδ coactivator Pgc - 1α, and citrate synthase was greater in gastrocnemius muscles from IL - 15Rα — KO relative to B6129 control (Figure 5C); however, levels of these genes were unchanged in spleen and kidney (data not shown).

Evan Paull, a graduate student in Stuart's lab at UC Santa Cruz (now at Columbia University), led the computational analyses, which involved integrating the phosphoproteomic data with genomic and gene expression datasets to provide a unified view of the activated signaling pathways in late stage prostate cancer.

In the current study, prediction analysis of gene expression data was implemented in order to identify the genes that are most useful to determine the state of cyclic changes in locomotor activitIn the current study, prediction analysis of gene expression data was implemented in order to identify the genes that are most useful to determine the state of cyclic changes in locomotor activitin order to identify the genes that are most useful to determine the state of cyclic changes in locomotor activitin locomotor activity.

Using quantitative RT - PCR we confirmed the expression profile of five genes (colony - stimulating factor 1 receptor [Csf1r], Cd68, Pex11a, Emr1, and Mcp1) in each of the 24 samples and found excellent agreement between the microarray and RT - PCR expression data (mean Pearson correlation coefficient = 0.91, microarray versus RT - PCR expression; Supplemental Table 3, http://www.jci.org/cgi/content/full/112/12/1796/DC1).

In particular, they examined what happens during gene expression — when genes copy data from DNA to RNA in order to create proteinIn particular, they examined what happens during gene expression — when genes copy data from DNA to RNA in order to create proteinin order to create proteins.

These are among the genes whose expression in our microarray expression data set correlated positively with body mass and adipocyte size.

These data demonstrate that variations in continuous quantitative traits such as body mass, adipocyte size, and BMI are correlated with quantitative variations in the expression of genes.

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 datIn 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 datin 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 datin the brain by conducting prediction analyses of the gene expression data.

«We analyzed dozens of variants of this gene and quantitatively measured expression in about 1,000 embryos, creating a quantitative data set that could be used to train mathematical models, utilizing parameter optimization,» Arnosti said.

For expression analysis in mice, we used microarray data as described above to select two internal control genes, cyclophilin B (Cphn2) and ribosomal protein S3 (Rps3).

The authors next took 997 tumors in the discovery set, integrated copy number and gene expression data, and performed clustering analyses to identify subgroups of tumors with distinct features and clinical outcomes.

A research team, led by Chao Cheng, Ph.D., Assistant Professor in the Department of Genetics at The Geisel School of Medicine at Dartmouth, used gene expression data from breast cancer patients to computationally infer the presence of different types of immune cells.

Most of these were invalidated when challenged with experimental data generated in Smith's laboratory, which measured gene expression of ESCs across 23 different cell culture conditions, all of which maintained pluripotency.

«This data allows classification of all human protein - coding genes into those coding for house - hold functions (present in all cells) and those that are tissue - specific genes with highly specialized expression in particular organs and tissues, such as kidney, liver, brain, heart, pancreas.

Gene Set Enrichment Analysis (GSEA) is a microarray data - mining technique used to determine whether there is coordinated differential expression or «enrichment» in a set of functionally related genes when comparing control and experimental samples [35].

Bethesda, USA (2016 - present) Research areas: Super-resolution microscopy, single - molecule imaging, gene expression, computational modeling and data analysis This section includes all projects during my postdoctoral research stay at the National Institutes of Health in Bethesda, MD (Unites States): (9) Understanding gene expression in eukaryotic cells»

The overarching goal of his research is to utilize high - throughput genomic data sets, mostly based on DNA sequencing, in order to build models that explain how gene expression is regulated.

He group is also interested in high throughput gene expression data analysis, especially using Bayesian network (BN) approaches.

Genetic data combined with information on gene expression and epigenomics in relevant tissues, and clinical information, can provide clues about the effects of genetic changes within an individual's genome that increase or decrease one's risk of developing type 2 diabetes and its complications, including heart and kidney disease.

All microarray data supporting the findings of this study have been deposited in the National Center for Biotechnology Information Gene Expression Omnibus (GEO) and are accessible through the GEO Series accession number GSE86885.

With the reference cell census data in hand, the research team is excited to conduct additional studies, including ones involving models or human patients with gastrointestinal conditions — Crohn's disease, ulcerative colitis, gastrointestinal cancers, forms of food allergy, etc. — aimed at identifying changes in gene expression and epithelial structure and function that could reveal new insights and opportunities for therapeutic development.

We focus on developing computational methods and tools for (a) analyzing large - scale gene expression data related to human cancer in search for gene markers and disease sub-categories, (b) identifying regulatory elements such as miRNA precursors and their targets in whole genomes of plants and mammals, (c) building theoretical models of gene regulatory networks.

The data discussed in this publication have been deposited in NCBI's Gene Expression Omnibus [25] and are accessible through GEO Series accession number GSE45534 [26].

On Supplementary Fig. 36c, we show the number of most informative genes (defined as the union of genes with importance > = 0.1 across all the 13 models in the case of the gene expression model, and the union of the genes with importance > = 0.1 across the 3 models in the case of TIN, with «importance» being a measure computed by xgboost) with respect to each tissue and data type.

The authors highlight the importance of measuring the variability of transcript expression and location in so many cells by using their data to discover genes with related functions in the cell.

The idea behind our work in bioinformatics is to build on existing methodologies regarding large - scale data analysis and to develop novel algorithms for processing and merging complex biological data from multiple sources such as gene expression data, sequence information, protein - to - protein interaction data, clinico - pathological data etc..