Down-weighting overlapping genes improves gene set analysis.
about
Evaluating gene set enrichment analysis via a hybrid data model.Reporter pathway analysis from transcriptome data: Metabolite-centric versus Reaction-centric approach.Combining multiple tools outperforms individual methods in gene set enrichment analysesRanking metrics in gene set enrichment analysis: do they matter?Multivariate inference of pathway activity in host immunity and response to therapeutics.Characterization of the myometrial transcriptome in women with an arrest of dilatation during laborA comparison of gene set analysis methods in terms of sensitivity, prioritization and specificity.DFLAT: functional annotation for human development.Identifying disease associated genes by network propagation.Transcriptomics of maternal and fetal membranes can discriminate between gestational-age matched preterm neonates with and without cognitive impairment diagnosed at 18-24 months.Comparative study on gene set and pathway topology-based enrichment methods.Integrative enrichment analysis: a new computational method to detect dysregulated pathways in heterogeneous samples.Bioconductor's EnrichmentBrowser: seamless navigation through combined results of set- & network-based enrichment analysis.Avoiding the pitfalls of gene set enrichment analysis with SetRank.LEGO: a novel method for gene set over-representation analysis by incorporating network-based gene weights.SPATIAL: A System-level PAThway Impact AnaLysis approach.In silico Pathway Activation Network Decomposition Analysis (iPANDA) as a method for biomarker development.Methods and approaches in the topology-based analysis of biological pathways.An approach to infer putative disease-specific mechanisms using neighboring gene networks.GSA-Lightning: ultra-fast permutation-based gene set analysis.Computational systems biology approaches for Parkinson's disease.Null diffusion-based enrichment for metabolomics data.Easy and efficient ensemble gene set testing with EGSEA.Semi-supervised identification of cancer subgroups using survival outcomes and overlapping grouping information.Using predictive specificity to determine when gene set analysis is biologically meaningful.DANUBE: Data-driven meta-ANalysis using UnBiased Empirical distributions-applied to biological pathway analysis.Characterization of visceral and subcutaneous adipose tissue transcriptome in pregnant women with and without spontaneous labor at term: implication of alternative splicing in the metabolic adaptations of adipose tissue to parturition.Pathway crosstalk effects: shrinkage and disentanglement using a Bayesian hierarchical model
P2860
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P2860
Down-weighting overlapping genes improves gene set analysis.
description
2012 nî lūn-bûn
@nan
2012 թուականի Յունիսին հրատարակուած գիտական յօդուած
@hyw
2012 թվականի հունիսին հրատարակված գիտական հոդված
@hy
2012年の論文
@ja
2012年論文
@yue
2012年論文
@zh-hant
2012年論文
@zh-hk
2012年論文
@zh-mo
2012年論文
@zh-tw
2012年论文
@wuu
name
Down-weighting overlapping genes improves gene set analysis.
@ast
Down-weighting overlapping genes improves gene set analysis.
@en
Down-weighting overlapping genes improves gene set analysis.
@nl
type
label
Down-weighting overlapping genes improves gene set analysis.
@ast
Down-weighting overlapping genes improves gene set analysis.
@en
Down-weighting overlapping genes improves gene set analysis.
@nl
prefLabel
Down-weighting overlapping genes improves gene set analysis.
@ast
Down-weighting overlapping genes improves gene set analysis.
@en
Down-weighting overlapping genes improves gene set analysis.
@nl
P2860
P356
P1433
P1476
Down-weighting overlapping genes improves gene set analysis.
@en
P2093
Adi Laurentiu Tarca
Gaurav Bhatti
P2860
P2888
P356
10.1186/1471-2105-13-136
P577
2012-06-19T00:00:00Z
P5875
P6179
1019407831