Quantifying the relationship between co-expression, co-regulation and gene function
about
Concise review: new paradigms for Down syndrome research using induced pluripotent stem cells: tackling complex human genetic diseasePredictive screening for regulators of conserved functional gene modules (gene batteries) in mammals.Optimal cDNA microarray design using expressed sequence tags for organisms with limited genomic informationDecoding the nucleoid organisation of Bacillus subtilis and Escherichia coli through gene expression dataA multistep bioinformatic approach detects putative regulatory elements in gene promotersAn improved distance measure between the expression profiles linking co-expression and co-regulation in mouse.Integration of 'omics' data in aging research: from biomarkers to systems biologyNetwork-Based Integration of GWAS and Gene Expression Identifies a HOX-Centric Network Associated with Serous Ovarian Cancer RiskBeyond modules and hubs: the potential of gene coexpression networks for investigating molecular mechanisms of complex brain disordersAlveolarization genes modulated by fetal tracheal occlusion in the rabbit model for congenital diaphragmatic hernia: a randomized studyMotif analysis unveils the possible co-regulation of chloroplast genes and nuclear genes encoding chloroplast proteinsCoexpression of nuclear receptors and histone methylation modifying genes in the testis: implications for endocrine disruptor modes of actionVSNL1 Co-Expression Networks in Aging Include Calcium Signaling, Synaptic Plasticity, and Alzheimer's Disease PathwaysInferring the Brassica rapa Interactome Using Protein-Protein Interaction Data from Arabidopsis thaliana.Identifying subspace gene clusters from microarray data using low-rank representation.Combined global localization analysis and transcriptome data identify genes that are directly coregulated by Adr1 and Cat8.Constructing biological pathways by a two-step counting approach.Integrative methods for analyzing big data in precision medicine.Analysis of time-series gene expression data: methods, challenges, and opportunities.Revealing complex function, process and pathway interactions with high-throughput expression and biological annotation data.Construction of a reference gene association network from multiple profiling data: application to data analysis.Extracting gene networks for low-dose radiation using graph theoretical algorithmsClustering biological annotations and gene expression data to identify putatively co-regulated biological processes.Context specific transcription factor prediction.Analyzing stochastic transcription to elucidate the nucleoid's organization.Topological comparison of methods for predicting transcriptional cooperativity in yeast.Modular organization of the white spruce (Picea glauca) transcriptome reveals functional organization and evolutionary signatures.The rules of gene expression in plants: organ identity and gene body methylation are key factors for regulation of gene expression in Arabidopsis thalianaPromoter-sharing by different genes in human genome--CPNE1 and RBM12 gene pair as an exampleEvolutionarily conserved transcriptional co-expression guiding embryonic stem cell differentiationDifferential expression and network inferences through functional data modelingIdentification of global transcriptional dynamicsConstruction and use of gene expression covariation matrix.The Arabidopsis wall associated kinase-like 10 gene encodes a functional guanylyl cyclase and is co-expressed with pathogen defense related genes.Intronic microRNAs support their host genes by mediating synergistic and antagonistic regulatory effects.Reconstruction of gene regulatory modules in cancer cell cycle by multi-source data integration.Co-expression module analysis reveals biological processes, genomic gain, and regulatory mechanisms associated with breast cancer progressionA systems approach to mapping transcriptional networks controlling surfactant homeostasis.Selection upon genome architecture: conservation of functional neighborhoods with changing genes.Identifying functional relationships within sets of co-expressed genes by combining upstream regulatory motif analysis and gene expression information.
P2860
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P2860
Quantifying the relationship between co-expression, co-regulation and gene function
description
2004 nî lūn-bûn
@nan
2004 թուականի Փետրուարին հրատարակուած գիտական յօդուած
@hyw
2004 թվականի փետրվարին հրատարակված գիտական հոդված
@hy
2004年の論文
@ja
2004年学术文章
@wuu
2004年学术文章
@zh-cn
2004年学术文章
@zh-hans
2004年学术文章
@zh-my
2004年学术文章
@zh-sg
2004年學術文章
@yue
name
Quantifying the relationship between co-expression, co-regulation and gene function
@ast
Quantifying the relationship between co-expression, co-regulation and gene function
@en
Quantifying the relationship between co-expression, co-regulation and gene function
@nl
type
label
Quantifying the relationship between co-expression, co-regulation and gene function
@ast
Quantifying the relationship between co-expression, co-regulation and gene function
@en
Quantifying the relationship between co-expression, co-regulation and gene function
@nl
prefLabel
Quantifying the relationship between co-expression, co-regulation and gene function
@ast
Quantifying the relationship between co-expression, co-regulation and gene function
@en
Quantifying the relationship between co-expression, co-regulation and gene function
@nl
P2860
P3181
P356
P1433
P1476
Quantifying the relationship between co-expression, co-regulation and gene function
@en
P2093
Dominic J Allocco
P2860
P2888
P3181
P356
10.1186/1471-2105-5-18
P407
P577
2004-02-25T00:00:00Z
P5875
P6179
1037393874