Prediction of breast cancer prognosis using gene set statistics provides signature stability and biological context.
about
Network biomarkers reveal dysfunctional gene regulations during disease progressionBiological network-driven gene selection identifies a stromal immune module as a key determinant of triple-negative breast carcinoma prognosis.Stable feature selection and classification algorithms for multiclass microarray data.Interpreting personal transcriptomes: personalized mechanism-scale profiling of RNA-seq dataCombining techniques for screening and evaluating interaction terms on high-dimensional time-to-event data.Breast cancer prognosis risk estimation using integrated gene expression and clinical dataKnowledge boosting: a graph-based integration approach with multi-omics data and genomic knowledge for cancer clinical outcome prediction.Novel gene sets improve set-level classification of prokaryotic gene expression data.Concordance of deregulated mechanisms unveiled in underpowered experiments: PTBP1 knockdown case studyThe influence of feature selection methods on accuracy, stability and interpretability of molecular signatures.Single sample expression-anchored mechanisms predict survival in head and neck cancer.A computational model to predict bone metastasis in breast cancer by integrating the dysregulated pathways.A novel model to combine clinical and pathway-based transcriptomic information for the prognosis prediction of breast cancerHepatitis C virus network based classification of hepatocellular cirrhosis and carcinomaA critical evaluation of network and pathway-based classifiers for outcome prediction in breast cancerComparative evaluation of set-level techniques in predictive classification of gene expression samples.'N-of-1-pathways' unveils personal deregulated mechanisms from a single pair of RNA-Seq samples: towards precision medicine.Curation-free biomodules mechanisms in prostate cancer predict recurrent disease.Ensemble classifier based on context specific miRNA regulation modules: a new method for cancer outcome prediction.Gene set bagging for estimating the probability a statistically significant result will replicate.Current composite-feature classification methods do not outperform simple single-genes classifiers in breast cancer prognosis.Design and multiseries validation of a web-based gene expression assay for predicting breast cancer recurrence and patient survivalAutocrine Activation of the Wnt/β-Catenin Pathway by CUX1 and GLIS1 in Breast Cancers.A hybrid approach of gene sets and single genes for the prediction of survival risks with gene expression data.Differentially Expressed Genes and Signature Pathways of Human Prostate Cancer.An argument for mechanism-based statistical inference in cancer.How interacting pathways are regulated by miRNAs in breast cancer subtypes.Prediction of breast cancer metastasis by gene expression profiles: a comparison of metagenes and single genes.Edge biomarkers for classification and prediction of phenotypes.Finding disagreement pathway signatures and constructing an ensemble model for cancer classificationPathway-Structured Predictive Model for Cancer Survival Prediction: A Two-Stage Approach.Subtype-dependent prognostic relevance of an interferon-induced pathway metagene in node-negative breast cancer.Clustering gene expression regulators: new approach to disease subtyping.Determining epithelial contribution to in vivo mesenchymal tumour expression signature using species-specific microarray profiling analysis of xenografts.Comparison of multiple transcriptomes exposes unified and divergent features of quiescent and activated skeletal muscle stem cells.Leveraging external knowledge on molecular interactions in classification methods for risk prediction of patients.
P2860
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P2860
Prediction of breast cancer prognosis using gene set statistics provides signature stability and biological context.
description
2010 nî lūn-bûn
@nan
2010 թուականի Մայիսին հրատարակուած գիտական յօդուած
@hyw
2010 թվականի մայիսին 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
Prediction of breast cancer pr ...... bility and biological context.
@ast
Prediction of breast cancer pr ...... bility and biological context.
@en
Prediction of breast cancer pr ...... bility and biological context.
@nl
type
label
Prediction of breast cancer pr ...... bility and biological context.
@ast
Prediction of breast cancer pr ...... bility and biological context.
@en
Prediction of breast cancer pr ...... bility and biological context.
@nl
prefLabel
Prediction of breast cancer pr ...... bility and biological context.
@ast
Prediction of breast cancer pr ...... bility and biological context.
@en
Prediction of breast cancer pr ...... bility and biological context.
@nl
P2093
P2860
P356
P1433
P1476
Prediction of breast cancer pr ...... bility and biological context.
@en
P2093
Adam Kowalczyk
Izhak Haviv
Justin Zobel
P2860
P2888
P356
10.1186/1471-2105-11-277
P577
2010-05-25T00:00:00Z
P5875
P6179
1006689340