Joint genetic analysis of gene expression data with inferred cellular phenotypes
about
Deep learning for computational biology.Learning transcriptional regulatory relationships using sparse graphical modelsPopulation differences in transcript-regulator expression quantitative trait lociPutting the Genome in Context: Gene-Environment Interactions in Type 2 Diabetes.GSVA: gene set variation analysis for microarray and RNA-seq data.Computational solutions for omics data.Normalizing RNA-sequencing data by modeling hidden covariates with prior knowledge.A modular framework for gene set analysis integrating multilevel omics data.Pathway-based factor analysis of gene expression data produces highly heritable phenotypes that associate with age.Inferring gene-phenotype associations via global protein complex network propagationGenetic interactions affecting human gene expression identified by variance association mapping.Gene co-expression network connectivity is an important determinant of selective constraint.Expression variation in connected recombinant populations of Arabidopsis thaliana highlights distinct transcriptome architectures.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.Dissecting high-dimensional phenotypes with bayesian sparse factor analysis of genetic covariance matrices.Nutrigenetics, nutrigenomics, and selenium.Generalised Anxiety Disorder--A Twin Study of Genetic Architecture, Genome-Wide Association and Differential Gene Expression.Functional genomic architecture of predisposition to voluntary exercise in mice: expression QTL in the brain.Gene-gene and gene-environment interactions detected by transcriptome sequence analysis in twins.Aging Shapes the Population-Mean and -Dispersion of Gene Expression in Human BrainsHEFT: eQTL analysis of many thousands of expressed genes while simultaneously controlling for hidden factorsReconstructing and analysing cellular states, space and time from gene expression profiles of many cells and single cells.Modelling local gene networks increases power to detect trans-acting genetic effects on gene expression.Extensive cis-regulatory variation robust to environmental perturbation in Arabidopsis.PERSONALIZED MEDICINE: FROM GENOTYPES AND MOLECULAR PHENOTYPES TOWARDS THERAPY.Mapping eQTL networks with mixed graphical Markov models.Controlling for Confounding Effects in Single Cell RNA Sequencing Studies Using both Control and Target Genesf-scLVM: scalable and versatile factor analysis for single-cell RNA-seq.RNA expression profiling of renal allografts in a non-human primate identifies variation in NK and endothelial gene expression.PERSONALIZED MEDICINE: FROM GENOTYPES AND MOLECULAR PHENOTYPES TOWARDS COMPUTED THERAPY.Genetics of -regulatory variation in gene expression
P2860
Q26740441-DA97A187-650C-461A-90B6-3842FA04E36AQ28730082-3107276A-437B-4CEF-9D83-89AE01841205Q28730906-12738EE4-6128-49D0-BBF2-CFB1BA1C6DF6Q30251394-C6BFDDD3-B97A-46BC-B353-DA2FBD0DDBE2Q30586230-984A07B7-7353-47AE-B272-C58617527A18Q30617899-93A0F9EA-BEC0-49A1-8C39-E74F451F03ACQ30656581-D2097DA4-DCDF-4B7B-8AE1-B1B9044ED1A1Q30663351-B43A4BA4-29B0-44A7-90F7-321C42585169Q30908386-0450EF1A-DC5C-44DC-B42B-83C9B2B67A66Q31025966-F2AAC8F9-CB86-4A02-9A1F-675D2B349898Q33597150-43573361-24B8-4F93-88C5-A27A697A3545Q33611013-D5D27A58-1615-479E-B161-0A9053B2DF46Q34209603-76991CAA-6C75-48DB-BCB8-E1790EC60948Q34255194-87B05996-2511-4BD9-95B8-32904D2CF360Q34270516-E945C644-7242-4200-8A87-CE26490A430CQ34699452-387BEB72-B813-4193-8726-59978DEA4685Q35711019-5EF22518-BC8D-4B8E-9C2D-752E8381A5BDQ35746041-8089A4CA-670D-4C93-B344-6BED06ADAAC6Q36029006-530C4E00-E8D0-429C-ADC1-AB1834514D3EQ36276125-6626D2A0-99F1-46A7-A178-CC753EF8857BQ37147054-FE1D81E6-EC6D-4A92-821C-17B8DD4FDE62Q37523930-51AEDA8F-F0F0-4353-9041-2B681DB7CFB2Q38566716-5EFAB7B6-9A91-4082-9F81-B285CBA5D803Q38609350-4723BE10-331E-4523-B0FF-D99C51061C54Q38957331-7A589F18-E2E2-4D9F-91CF-FF518911D3E8Q40814838-C30EA419-283E-47C7-860B-E478D7FD81B3Q42551490-042D761F-CA59-4309-BB05-F0171FBC44BEQ42670551-1DF4D34A-35CB-4401-AAFF-567389819D12Q46726359-50447F8F-F438-4EC0-965E-51EECDF71CD3Q47234619-7B0C7C05-4402-4A02-9892-5B74C18FA9EDQ55376634-DD7F5733-76D1-4C4D-99C6-2E55A1810EE1Q56534461-6422C77E-A242-491C-BAA2-499BB7D954AC
P2860
Joint genetic analysis of gene expression data with inferred cellular phenotypes
description
2011 nî lūn-bûn
@nan
2011 թուականի Յունուարին հրատարակուած գիտական յօդուած
@hyw
2011 թվականի հունվարին հրատարակված գիտական հոդված
@hy
2011年の論文
@ja
2011年論文
@yue
2011年論文
@zh-hant
2011年論文
@zh-hk
2011年論文
@zh-mo
2011年論文
@zh-tw
2011年论文
@wuu
name
Joint genetic analysis of gene expression data with inferred cellular phenotypes
@ast
Joint genetic analysis of gene expression data with inferred cellular phenotypes
@en
type
label
Joint genetic analysis of gene expression data with inferred cellular phenotypes
@ast
Joint genetic analysis of gene expression data with inferred cellular phenotypes
@en
prefLabel
Joint genetic analysis of gene expression data with inferred cellular phenotypes
@ast
Joint genetic analysis of gene expression data with inferred cellular phenotypes
@en
P2860
P50
P1433
P1476
Joint genetic analysis of gene expression data with inferred cellular phenotypes
@en
P2093
P2860
P304
P356
10.1371/JOURNAL.PGEN.1001276
P577
2011-01-20T00:00:00Z