Detecting disease associated modules and prioritizing active genes based on high throughput data.
about
Strategies for Integrated Analysis of Genetic, Epigenetic, and Gene Expression Variation in Cancer: Addressing the ChallengesIntegrative approaches for finding modular structure in biological networksPathway-Based Genomics Prediction using Generalized Elastic NetReconciling differential gene expression data with molecular interaction networksIdentification of mutated core cancer modules by integrating somatic mutation, copy number variation, and gene expression data.Functional module search in protein networks based on semantic similarity improves the analysis of proteomics data.Network-based analysis of omics data: the LEAN methodIdentifying core gene modules in glioblastoma based on multilayer factor-mediated dysfunctional regulatory networks through integrating multi-dimensional genomic data.A comparative study of improvements Pre-filter methods bring on feature selection using microarray dataInferring gene-phenotype associations via global protein complex network propagationIntegrative analysis for identifying joint modular patterns of gene-expression and drug-response data.Optimization of cell lines as tumour models by integrating multi-omics data.Discovering conditional co-regulated protein complexes by integrating diverse data sources.On the performance of de novo pathway enrichment.ExprEssence--revealing the essence of differential experimental data in the context of an interaction/regulation net-workIntegrative analysis reveals disease-associated genes and biomarkers for prostate cancer progression.Disease gene interaction pathways: a potential framework for how disease genes associate by disease-risk modules.Integrating biological pathways and genomic profiles with ChiBE 2.A computable cellular stress network model for non-diseased pulmonary and cardiovascular tissue.An update on the strategies in multicomponent activity monitoring within the phytopharmaceutical fieldSensitive detection of pathway perturbations in cancersInsights into the pathogenesis of axial spondyloarthropathy from network and pathway analysisPrediction of human disease-related gene clusters by clustering analysisDrug target inference through pathway analysis of genomics data.Active subnetwork recovery with a mechanism-dependent scoring function; with application to angiogenesis and organogenesis studiesCOSINE: COndition-SpecIfic sub-NEtwork identification using a global optimization method.An integer linear programming approach for finding deregulated subgraphs in regulatory networks.Network diffusion-based analysis of high-throughput data for the detection of differentially enriched modules.The network organization of cancer-associated protein complexes in human tissuesNetwork-based drug repositioning.Biomarker gene signature discovery integrating network knowledge.Network stratification analysis for identifying function-specific network layers.Integrating Heterogeneous Datasets for Cancer Module Identification.Robust de novo pathway enrichment with KeyPathwayMiner 5.GTA: a game theoretic approach to identifying cancer subnetwork markers.Identification of interconnected markers for T-cell acute lymphoblastic leukemiaCytoGTA: A cytoscape plugin for identifying discriminative subnetwork markers using a game theoretic approach.Efficient methods for identifying mutated driver pathways in cancer.De novo pathway-based biomarker identification.Network module identification-A widespread theoretical bias and best practices.
P2860
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P2860
Detecting disease associated modules and prioritizing active genes based on high throughput data.
description
2010 nî lūn-bûn
@nan
2010 թուականի Յունուարին հրատարակուած գիտական յօդուած
@hyw
2010 թվականի հունվարին 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
Detecting disease associated m ...... based on high throughput data.
@ast
Detecting disease associated m ...... based on high throughput data.
@en
Detecting disease associated m ...... based on high throughput data.
@nl
type
label
Detecting disease associated m ...... based on high throughput data.
@ast
Detecting disease associated m ...... based on high throughput data.
@en
Detecting disease associated m ...... based on high throughput data.
@nl
prefLabel
Detecting disease associated m ...... based on high throughput data.
@ast
Detecting disease associated m ...... based on high throughput data.
@en
Detecting disease associated m ...... based on high throughput data.
@nl
P2093
P2860
P356
P1433
P1476
Detecting disease associated m ...... based on high throughput data.
@en
P2093
Luonan Chen
Shihua Zhang
Xiang-Sun Zhang
Yu-Qing Qiu
P2860
P2888
P356
10.1186/1471-2105-11-26
P577
2010-01-13T00:00:00Z