Statistical methods for gene set co-expression analysis.
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Advanced Applications of RNA Sequencing and ChallengesHarnessing the complexity of gene expression data from cancer: from single gene to structural pathway methodsAnalyzing LC/MS metabolic profiling data in the context of existing metabolic networks.Network completion for static gene expression data.DECODE: an integrated differential co-expression and differential expression analysis of gene expression data.A semi-parametric Bayesian model for unsupervised differential co-expression analysisDynamic protein interaction modules in human hepatocellular carcinoma progressionDiffCoEx: a simple and sensitive method to find differentially coexpressed gene modules.Quantifying differential gene connectivity between disease states for objective identification of disease-relevant genes.Deregulation upon DNA damage revealed by joint analysis of context-specific perturbation data.Differential C3NET reveals disease networks of direct physical interactions.Link-based quantitative methods to identify differentially coexpressed genes and gene pairs.Assessment method for a power analysis to identify differentially expressed pathways.Centrality-based pathway enrichment: a systematic approach for finding significant pathways dominated by key genesDissection of regulatory networks that are altered in disease via differential co-expression.New network topology approaches reveal differential correlation patterns in breast cancer.Microarray enriched gene rankDCGL v2.0: an R package for unveiling differential regulation from differential co-expressionEDDY: a novel statistical gene set test method to detect differential genetic dependencies.Nonlinear dependence in the discovery of differentially expressed genes.Exploiting identifiability and intergene correlation for improved detection of differential expressionMeDiA: Mean Distance Association and Its Applications in Nonlinear Gene Set AnalysisCoGA: An R Package to Identify Differentially Co-Expressed Gene Sets by Analyzing the Graph Spectra.ChiNet uncovers rewired transcription subnetworks in tolerant yeast for advanced biofuels conversion.Whole Blood Gene Expression Profiles of Patients with a Past Aneurysmal Subarachnoid Hemorrhage.A novel method to quantify gene set functional association based on gene ontology.Gene set analysis using sufficient dimension reduction.ESEA: Discovering the Dysregulated Pathways based on Edge Set Enrichment AnalysisUnsupervised gene set testing based on random matrix theory.DGCA: A comprehensive R package for Differential Gene Correlation Analysis.GSAR: Bioconductor package for Gene Set analysis in RLarge-scale gene network analysis reveals the significance of extracellular matrix pathway and homeobox genes in acute myeloid leukemia: an introduction to the Pigengene package and its applications.KNOWLEDGE-ASSISTED APPROACH TO IDENTIFY PATHWAYS WITH DIFFERENTIAL DEPENDENCIES.Gene Expression Architecture of Mouse Dorsal and Tail Skin Reveals Functional Differences in Inflammation and Cancer.Plasma microRNA biomarker detection for mild cognitive impairment using differential correlation analysis.LncSubpathway: a novel approach for identifying dysfunctional subpathways associated with risk lncRNAs by integrating lncRNA and mRNA expression profiles and pathway topologies.BFDCA: A Comprehensive Tool of Using Bayes Factor for Differential Co-Expression Analysis.Extracting the Strongest Signals from Omics Data: Differentially Expressed Pathways and Beyond.Identification of Transcriptional Modules and Key Genes in Chickens Infected with Salmonella enterica Serovar Pullorum Using Integrated Coexpression AnalysesIdentification of perturbed signaling pathways from gene expression data using information divergence.
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Statistical methods for gene set co-expression analysis.
description
article científic
@ca
article scientifique
@fr
articolo scientifico
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artigo científico
@pt
bilimsel makale
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scientific article published on 18 August 2009
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vedecký článok
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vetenskaplig artikel
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videnskabelig artikel
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vědecký článek
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name
Statistical methods for gene set co-expression analysis.
@en
Statistical methods for gene set co-expression analysis.
@nl
type
label
Statistical methods for gene set co-expression analysis.
@en
Statistical methods for gene set co-expression analysis.
@nl
prefLabel
Statistical methods for gene set co-expression analysis.
@en
Statistical methods for gene set co-expression analysis.
@nl
P2860
P356
P1433
P1476
Statistical methods for gene set co-expression analysis.
@en
P2093
YounJeong Choi
P2860
P304
P356
10.1093/BIOINFORMATICS/BTP502
P407
P577
2009-08-18T00:00:00Z