A network-based gene expression signature informs prognosis and treatment for colorectal cancer patients.
about
Integrative analysis of cancer imaging readouts by networksThe `dnet’ approach promotes emerging research on cancer patient survivalIdentifying prognostic features by bottom-up approach and correlating to drug repositioningIdentification of commonly dysregulated genes in colorectal cancer by integrating analysis of RNA-Seq data and qRT-PCR validation.Logic Learning Machine and standard supervised methods for Hodgkin's lymphoma prognosis using gene expression data and clinical variables.Network-based biomarkers enhance classical approaches to prognostic gene expression signatures.Interferon signature in the blood in inflammatory common variable immune deficiencyDeciphering genomic alterations in colorectal cancer through transcriptional subtype-based network analysisIntegrative topological analysis of mass spectrometry data reveals molecular features with clinical relevance in esophageal squamous cell carcinoma.Exome sequencing of a colorectal cancer family reveals shared mutation pattern and predisposition circuitry along tumor pathwaysColoFinder: a prognostic 9-gene signature improves prognosis for 871 stage II and III colorectal cancer patients.Identification of 42 Genes Linked to Stage II Colorectal Cancer Metastatic Relapse.WEB-based GEne SeT AnaLysis Toolkit (WebGestalt): update 2013Integrative genomics analysis identifies candidate drivers at 3q26-29 amplicon in squamous cell carcinoma of the lungDiscovery of genes from feces correlated with colorectal cancer progressionIdentification of metastasis-associated genes in colorectal cancer through an integrated genomic and transcriptomic analysis.Nuclear factor of activated T-cell activity is associated with metastatic capacity in colon cancer.Evaluation of frozen tissue-derived prognostic gene expression signatures in FFPE colorectal cancer samples.SNRFCB: sub-network based random forest classifier for predicting chemotherapy benefit on survival for cancer treatment.DISNOR: a disease network open resource.Including network knowledge into Cox regression models for biomarker signature discovery.Network-based sub-network signatures unveil the potential for acute myeloid leukemia therapy.
P2860
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P2860
A network-based gene expression signature informs prognosis and treatment for colorectal cancer patients.
description
2012 nî lūn-bûn
@nan
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
2012年论文
@zh
2012年论文
@zh-cn
name
A network-based gene expressio ...... or colorectal cancer patients.
@ast
A network-based gene expressio ...... or colorectal cancer patients.
@en
type
label
A network-based gene expressio ...... or colorectal cancer patients.
@ast
A network-based gene expressio ...... or colorectal cancer patients.
@en
prefLabel
A network-based gene expressio ...... or colorectal cancer patients.
@ast
A network-based gene expressio ...... or colorectal cancer patients.
@en
P2093
P2860
P1433
P1476
A network-based gene expressio ...... or colorectal cancer patients.
@en
P2093
Bing Zhang
Mingguang Shi
R Daniel Beauchamp
P2860
P304
P356
10.1371/JOURNAL.PONE.0041292
P407
P577
2012-07-23T00:00:00Z