Prognostic gene signatures for patient stratification in breast cancer: accuracy, stability and interpretability of gene selection approaches using prior knowledge on protein-protein interactions.
about
Biological network-driven gene selection identifies a stromal immune module as a key determinant of triple-negative breast carcinoma prognosis.Pareto Optimization Identifies Diverse Set of Phosphorylation Signatures Predicting Response to Treatment with DasatinibNetwork and data integration for biomarker signature discovery via network smoothed T-statistics.Data Requirements for Model-Based Cancer Prognosis PredictionData-Driven Metabolic Pathway Compositions Enhance Cancer Survival Prediction.Robust clinical outcome prediction based on Bayesian analysis of transcriptional profiles and prior causal networksComputational prognostic indicators for breast cancer.Network-based biomarkers enhance classical approaches to prognostic gene expression signatures.Network-constrained group lasso for high-dimensional multinomial classification with application to cancer subtype predictionCurrent composite-feature classification methods do not outperform simple single-genes classifiers in breast cancer prognosis.A Stromal Immune Module Correlated with the Response to Neoadjuvant Chemotherapy, Prognosis and Lymphocyte Infiltration in HER2-Positive Breast Carcinoma Is Inversely Correlated with Hormonal Pathways.FERAL: network-based classifier with application to breast cancer outcome prediction.Differential distribution improves gene selection stability and has competitive classification performance for patient survival.Biomarker gene signature discovery integrating network knowledge.Gene expression profiling for targeted cancer treatment.Cancer bioinformatics: a new approach to systems clinical medicine.Including network knowledge into Cox regression models for biomarker signature discovery.Robust phenotype prediction from gene expression data using differential shrinkage of co-regulated genes.New insight for pharmacogenomics studies from the transcriptional analysis of two large-scale cancer cell line panels.De novo pathway-based biomarker identification.RRHGE: a novel approach to classify the estrogen receptor based breast cancer subtypes.From hype to reality: data science enabling personalized medicine
P2860
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P2860
Prognostic gene signatures for patient stratification in breast cancer: accuracy, stability and interpretability of gene selection approaches using prior knowledge on protein-protein interactions.
description
2012 nî lūn-bûn
@nan
2012 թուականի Մայիսին հրատարակուած գիտական յօդուած
@hyw
2012 թվականի մայիսին 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
Prognostic gene signatures for ...... protein-protein interactions.
@ast
Prognostic gene signatures for ...... protein-protein interactions.
@en
Prognostic gene signatures for ...... protein-protein interactions.
@nl
type
label
Prognostic gene signatures for ...... protein-protein interactions.
@ast
Prognostic gene signatures for ...... protein-protein interactions.
@en
Prognostic gene signatures for ...... protein-protein interactions.
@nl
prefLabel
Prognostic gene signatures for ...... protein-protein interactions.
@ast
Prognostic gene signatures for ...... protein-protein interactions.
@en
Prognostic gene signatures for ...... protein-protein interactions.
@nl
P2860
P356
P1433
P1476
Prognostic gene signatures for ...... n protein-protein interactions
@en
P2093
Holger Fröhlichholger Fröhlich
Yupeng Cun
P2860
P2888
P356
10.1186/1471-2105-13-69
P577
2012-05-01T00:00:00Z
P5875
P6179
1048982272