Learning gene networks under SNP perturbations using eQTL datasets.
about
Bayesian network reconstruction using systems genetics data: comparison of MCMC methodsNode-Structured Integrative Gaussian Graphical Model Guided by Pathway Information.A network based covariance test for detecting multivariate eQTL in saccharomyces cerevisiae.Genome-Wide Analysis Identifies Germ-Line Risk Factors Associated with Canine Mammary Tumours.Learning mixed graphical models with separate sparsity parameters and stability-based model selection.eQTL networks unveil enriched mRNA master integrators downstream of complex disease-associated SNPs.A deep auto-encoder model for gene expression prediction.Heterogeneous reciprocal graphical models.Expression quantitative trait loci (eQTLs) in human placentas suggest developmental origins of complex diseases.Bayesian graphical models for computational network biology.Genetics of -regulatory variation in gene expression
P2860
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P2860
Learning gene networks under SNP perturbations using eQTL datasets.
description
2014 nî lūn-bûn
@nan
2014 թուականի Փետրուարին հրատարակուած գիտական յօդուած
@hyw
2014 թվականի փետրվարին հրատարակված գիտական հոդված
@hy
2014年の論文
@ja
2014年論文
@yue
2014年論文
@zh-hant
2014年論文
@zh-hk
2014年論文
@zh-mo
2014年論文
@zh-tw
2014年论文
@wuu
name
Learning gene networks under SNP perturbations using eQTL datasets.
@ast
Learning gene networks under SNP perturbations using eQTL datasets.
@en
type
label
Learning gene networks under SNP perturbations using eQTL datasets.
@ast
Learning gene networks under SNP perturbations using eQTL datasets.
@en
prefLabel
Learning gene networks under SNP perturbations using eQTL datasets.
@ast
Learning gene networks under SNP perturbations using eQTL datasets.
@en
P2860
P1476
Learning gene networks under SNP perturbations using eQTL datasets.
@en
P2093
Lingxue Zhang
Seyoung Kim
P2860
P304
P356
10.1371/JOURNAL.PCBI.1003420
P577
2014-02-27T00:00:00Z