A Bayesian framework to account for complex non-genetic factors in gene expression levels greatly increases power in eQTL studies.
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Exploiting gene expression variation to capture gene-environment interactions for diseaseDeep learning for computational biology.Yeast as a cell factory: current state and perspectives.Genetic Control of Chromatin States in Humans Involves Local and Distal Chromosomal InteractionsStatistical Methods in Integrative GenomicsIdentification, replication, and functional fine-mapping of expression quantitative trait loci in primary human liver tissueGenome-wide association study and gene expression analysis identifies CD84 as a predictor of response to etanercept therapy in rheumatoid arthritisContext Specific and Differential Gene Co-expression Networks via Bayesian BiclusteringLocal Adaptation of Sun-Exposure-Dependent Gene Expression Regulation in Human SkinExon-specific QTLs skew the inferred distribution of expression QTLs detected using gene expression array dataMultiple Hepatic Regulatory Variants at the GALNT2 GWAS Locus Associated with High-Density Lipoprotein CholesterolRNA-Seq optimization with eQTL gold standards.Identification of well-differentiated gene expressions between Han Chinese and Japanese using genome-wide microarray data analysis.Normalizing RNA-sequencing data by modeling hidden covariates with prior knowledge.Variation-preserving normalization unveils blind spots in gene expression profiling.Detecting and correcting systematic variation in large-scale RNA sequencing datasvaseq: removing batch effects and other unwanted noise from sequencing dataComputational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells.Bayesian network reconstruction using systems genetics data: comparison of MCMC methodsHow data analysis affects power, reproducibility and biological insight of RNA-seq studies in complex datasetsmRIN for direct assessment of genome-wide and gene-specific mRNA integrity from large-scale RNA-sequencing data.MODEM: multi-omics data envelopment and mining in maize.Detecting Sources of Transcriptional Heterogeneity in Large-Scale RNA-Seq Data Sets.Finding alternative expression quantitative trait loci by exploring sparse model spaceType I interferon signaling genes in recurrent major depression: increased expression detected by whole-blood RNA sequencingPrediction of gene expression with cis-SNPs using mixed models and regularization methodsAn independent component analysis confounding factor correction framework for identifying broad impact expression quantitative trait lociJoint genetic analysis of gene expression data with inferred cellular phenotypesThe architecture of gene regulatory variation across multiple human tissues: the MuTHER study.Assessing multivariate gene-metabolome associations with rare variants using Bayesian reduced rank regression.Identification of a Sjögren's syndrome susceptibility locus at OAS1 that influences isoform switching, protein expression, and responsiveness to type I interferons.Relating CNVs to transcriptome data at fine resolution: assessment of the effect of variant size, type, and overlap with functional regions.Massively parallel quantification of the regulatory effects of noncoding genetic variation in a human cohort.Joint modelling of confounding factors and prominent genetic regulators provides increased accuracy in genetical genomics studies.Expression variation in connected recombinant populations of Arabidopsis thaliana highlights distinct transcriptome architectures.Grasping nettles: cellular heterogeneity and other confounders in epigenome-wide association studiesPatterns of cis regulatory variation in diverse human populations.Using probabilistic estimation of expression residuals (PEER) to obtain increased power and interpretability of gene expression analysesExtent, causes, and consequences of small RNA expression variation in human adipose tissue.Transcriptome and genome sequencing uncovers functional variation in humans
P2860
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P2860
A Bayesian framework to account for complex non-genetic factors in gene expression levels greatly increases power in eQTL studies.
description
2010 nî lūn-bûn
@nan
2010 թուականի Մայիսին հրատարակուած գիտական յօդուած
@hyw
2010 թվականի մայիսին հրատարակված գիտական հոդված
@hy
2010年の論文
@ja
2010年論文
@yue
2010年論文
@zh-hant
2010年論文
@zh-hk
2010年論文
@zh-mo
2010年論文
@zh-tw
2010年论文
@wuu
name
A Bayesian framework to accoun ...... creases power in eQTL studies.
@ast
A Bayesian framework to accoun ...... creases power in eQTL studies.
@en
A Bayesian framework to accoun ...... creases power in eQTL studies.
@nl
type
label
A Bayesian framework to accoun ...... creases power in eQTL studies.
@ast
A Bayesian framework to accoun ...... creases power in eQTL studies.
@en
A Bayesian framework to accoun ...... creases power in eQTL studies.
@nl
prefLabel
A Bayesian framework to accoun ...... creases power in eQTL studies.
@ast
A Bayesian framework to accoun ...... creases power in eQTL studies.
@en
A Bayesian framework to accoun ...... creases power in eQTL studies.
@nl
P2860
P50
P1476
A Bayesian framework to accoun ...... creases power in eQTL studies.
@en
P2093
P2860
P304
P356
10.1371/JOURNAL.PCBI.1000770
P577
2010-05-06T00:00:00Z