Sentences with phrase «on gene expression data»

Based on our gene expression data, FGF2 could affect oocyte maturation in a paracrine manner via the cumulus cells.

Garcia - Ojalvo expressed surprise that a network based only on gene expression data could predict, with relative accuracy, the effect of multiple genetic interactions.

The project was based on gene expression data from the modENCODE project, which aims to provide the scientific community with a comprehensive encyclopedia of functional elements of the genome of model organisms.

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.

Not exact matches

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.

The data are already being used to improve gene information and expression on VectorBase, a National Institute of Allergy and Infectious Diseases resource center for the scientific community.

Toxicologist Craig Harris of the University of Michigan, Ann Arbor, who has studied thalidomide's effects on gene expression, says that the new data are consistent with some theories of the drug's action, however.

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 — Cas9.

A: Sheltzer: Professionally, we're working on a paper together using Joan's data - analysis ability to parse through gene - expression data from more than 20,000 cancer patients.

Still others are working to overlay gene - expression patterns, electrophysiological measurements or other functional data on those maps.

It contains data on the electrical properties of about 300 cortical neurons taken from 36 patients and 3D reconstructions of 100 of those cells, plus gene expression data from 16,000 neurons from three other patients.

They are also developing sgRNAs that will enable even more fine - tuning of gene expression levels and on software for analyzing the data.

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.

These classifications are based on large amounts of data inclusing clinical data, somatic mutations, gene and alternative transcript expression, or structural DNA modification, and involve high - dimensional statistical machine learning techniques.

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.

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.

We investigate the impact of PMI on gene expression using data from multiple tissues of post-mortem donors obtained from the GTEx project.

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.

Much of the research carried out today on rodent models generates high resolution image data, allowing characterization and analysis of brain molecular distribution, gene expression, and connectivity.

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..

As calculations are based on DNA sequence data and not physical measurements, it can tease apart the genetically determined component of gene expression from the effects of the trait itself (avoiding reverse causality) and other factors such as environment.

A reliability score is set manually for all genes and indicates the level of reliability of the analyzed protein expression pattern based on available protein / RNA / gene characterization data from both HPA and the UniProtKB / Swiss - Prot database.

This Vignette animates the creation of a data driven classification of the cell types in the mouse visual cortex based on the individual cells» gene expression.

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.»

This is in accordance with previous reports that decitabine and 5 - azacytidine produce a marked synergistic effect in combination with suberoylanilide hydroxamic acid and romidepsin in T - lymphoma cell lines by modulating cell cycle arrest and apoptosis.26, 27 As a mechanism of action, KMT2D mutations of B - lymphoma cells promote malignant outgrowth by perturbing methylation of H3K4 that affect the JAK - STAT, Toll - like receptor, or B - cell receptor pathway.28, 29 Here our study indicated that dual treatment with chidamide and decitabine enhanced the interaction of KMT2D with the transcription factor PU.1, thereby inactivating the H3K4me - associated signaling pathway MAPK, which is constitutively activated in T - cell lymphoma.13, 30,31 The transcription factor PU.1 is involved in the development of all hematopoietic lineages32 and regulates lymphoid cell growth and transformation.33 Aberrant PU.1 expression promotes acute myeloid leukemia and is related to the pathogenesis of multiple myeloma via the MAPK pathway.34, 35 On the other hand, PU.1 is also shown to interact with chromatin remodeler and DNA methyltransferease to control hematopoiesis and suppress leukemia.36 Our data thus suggested that the combined action of chidamide and decitabine may interfere with the differentiation and / or viability of PTCL - NOS through a PU.1 - dependent gene expression program.

During the past decade, data on the putative roles of STAT proteins in mediating gene expression without tyrosine phosphorylation have been accumulating.

A reliability score is set for all genes and indicates the level of reliability of the analyzed protein expression pattern based on available protein / RNA / gene characterization data.

There's also a lot of work under way for TCGA, not just the 6K capture project, but also adjunct analyses of gene expression, DNA copy number, microRNA, and DNA methylation data being generated on TCGA samples.

«Based on our data, which is comprised of gene expression across 16 brain regions, we found that the most distinct region, i.e. the region where we observe more human - specific differences in gene expression, is the striatum, a region involved in motor coordination, reward, and decision - making,» lead author André M. Sousa of the Yale School of Medicine and the Kavli Institute for Neuroscience told Seeker.

Researchers will use GTEx data to follow up on findings from genome - wide association studies and as a resource for the general study of gene expression networks.

Although the database described here was based on the gene expression profiling results of the FunGenES consortium, it can be easily adapted to incorporate available or future genomics data obtained in ES cells.

Her research interests are focused on the reconstruction of cell response networks from integrated gene and protein expression data to enable predictive mechanistic modeling of disease and toxicity pathways.

To annotate the genome, the team generated transcriptome sequence data — which can be used to measure gene expression based on RNA levels — in 12 different tissues types.

Title: Digital spatial profiling platform allows for spatially - resolved, high - plex quantification of mRNA distribution and abundance on FFPE and fresh frozen tissue sections Date / Time: Tuesday, April 17 2018, 8am - 12:00 pm CT Author: Daniel Zollinger, NanoString Poster # / Location: 3434 / Section 18, Board 16 Hyperlink: http://www.abstractsonline.com/pp8/#!/4562/presentation/7119 Digital Spatial Profiling can be used to obtain high - plex, spatial mRNA expression data (10's to 100's of genes) and protein expression data on FFPE and fresh frozen tissue sections.

In this activity students analyze data on the expression of the tb1 gene and use it to formulate an explanation as to how a specific difference in the corn version of the gene explains the phenotype of less branching.