Thousands of samples are needed to generate a robust gene list for predicting outcome in cancer
about
Predictive biomarker discovery through the parallel integration of clinical trial and functional genomics datasetsThe MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurementsPattern recognition in bioinformaticsPathway mapping and development of disease-specific biomarkers: protein-based network biomarkersA mouse stromal response to tumor invasion predicts prostate and breast cancer patient survivalA network-based method to evaluate quality of reproducibility of differential expression in cancer genomics studiesParallel evolution under chemotherapy pressure in 29 breast cancer cell lines results in dissimilar mechanisms of resistancePost-genomic clinical trials: the perspective of ACGTIntegrative computational biology for cancer researchNetwork-based classification of breast cancer metastasisMetabolomics in the study of kidney diseasesImaging biomarker roadmap for cancer studiesBiologically inspired intelligent decision making: a commentary on the use of artificial neural networks in bioinformaticsDetecting multivariate differentially expressed genes.Prognostic gene signatures for non-small-cell lung cancer.On reliable discovery of molecular signaturesFunctional analysis of multiple genomic signatures demonstrates that classification algorithms choose phenotype-related genes.The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models.Exploiting the noise: improving biomarkers with ensembles of data analysis methodologies.Direct integration of intensity-level data from Affymetrix and Illumina microarrays improves statistical power for robust reanalysis.Molecular signature of cancer at gene level or pathway level? Case studies of colorectal cancer and prostate cancer microarray data.Covariance adjustment for batch effect in gene expression dataDeciphering global signal features of high-throughput array data from cancers.Breast cancer prognosis risk estimation using integrated gene expression and clinical dataGene expression profiling for molecular distinction and characterization of laser captured primary lung cancersTest of four colon cancer risk-scores in formalin fixed paraffin embedded microarray gene expression data.Accurate and reliable cancer classification based on probabilistic inference of pathway activity.Topologically inferring pathway activity toward precise cancer classification via integrating genomic and metabolomic data: prostate cancer as a case.Quantifying stability in gene list ranking across microarray derived clinical biomarkers.Data Requirements for Model-Based Cancer Prognosis PredictionMeta-analysis of gene expression data: a predictor-based approach.Cross-study analysis of gene expression data for intermediate neuroblastoma identifies two biological subtypes.Overcoming the matched-sample bottleneck: an orthogonal approach to integrate omic data.INDEED: Integrated differential expression and differential network analysis of omic data for biomarker discovery.t-Test at the Probe Level: An Alternative Method to Identify Statistically Significant Genes for Microarray Data.Network-Based Biomedical Data Analysis.Interpretation of genomic data: questions and answers.Meta-analysis of microarray studies reveals a novel hematopoietic progenitor cell signature and demonstrates feasibility of inter-platform data integration.Selecting normalization genes for small diagnostic microarrays.A mixture model approach to the tests of concordance and discordance between two large-scale experiments with two-sample groups.
P2860
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P2860
Thousands of samples are needed to generate a robust gene list for predicting outcome in cancer
description
2006 nî lūn-bûn
@nan
2006 թուականի Ապրիլին հրատարակուած գիտական յօդուած
@hyw
2006 թվականի ապրիլին հրատարակված գիտական հոդված
@hy
2006年の論文
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2006年論文
@yue
2006年論文
@zh-hant
2006年論文
@zh-hk
2006年論文
@zh-mo
2006年論文
@zh-tw
2006年论文
@wuu
name
Thousands of samples are neede ...... r predicting outcome in cancer
@ast
Thousands of samples are neede ...... r predicting outcome in cancer
@en
Thousands of samples are neede ...... r predicting outcome in cancer
@nl
type
label
Thousands of samples are neede ...... r predicting outcome in cancer
@ast
Thousands of samples are neede ...... r predicting outcome in cancer
@en
Thousands of samples are neede ...... r predicting outcome in cancer
@nl
prefLabel
Thousands of samples are neede ...... r predicting outcome in cancer
@ast
Thousands of samples are neede ...... r predicting outcome in cancer
@en
Thousands of samples are neede ...... r predicting outcome in cancer
@nl
P2093
P2860
P356
P1476
Thousands of samples are neede ...... r predicting outcome in cancer
@en
P2093
Eytan Domany
Liat Ein-Dor
P2860
P304
P356
10.1073/PNAS.0601231103
P407
P577
2006-04-03T00:00:00Z