The removal of multiplicative, systematic bias allows integration of breast cancer gene expression datasets - improving meta-analysis and prediction of prognosis.
about
Accurate prediction of response to endocrine therapy in breast cancer patients: current and future biomarkersCorrecting for intra-experiment variation in Illumina BeadChip data is necessary to generate robust gene-expression profilesDirect integration of intensity-level data from Affymetrix and Illumina microarrays improves statistical power for robust reanalysis.Unlocking the potential of publicly available microarray data using inSilicoDb and inSilicoMerging R/Bioconductor packages.A new statistic for identifying batch effects in high-throughput genomic data that uses guided principal component analysis.A comparative evaluation of data-merging and meta-analysis methods for reconstructing gene-gene interactionsBatch effect confounding leads to strong bias in performance estimates obtained by cross-validation.Removing batch effects in analysis of expression microarray data: an evaluation of six batch adjustment methods.Sprouty 2 is an independent prognostic factor in breast cancer and may be useful in stratifying patients for trastuzumab therapyCyclin D1, Id1 and EMT in breast cancer.Relative impact of key sources of systematic noise in Affymetrix and Illumina gene-expression microarray experiments.Batch effect correction for genome-wide methylation data with Illumina Infinium platform.The embryonic transcription cofactor LBH is a direct target of the Wnt signaling pathway in epithelial development and in aggressive basal subtype breast cancersBatch effect removal methods for microarray gene expression data integration: a survey.MEK1 is associated with carboplatin resistance and is a prognostic biomarker in epithelial ovarian cancer.Wnt pathway activity in breast cancer sub-types and stem-like cellsFunctional genomic and proteomic analysis reveals disruption of myelin-related genes and translation in a mouse model of early life neglectDecreased expression of Yes-associated protein is associated with outcome in the luminal A breast cancer subgroup and with an impaired tamoxifen response.Comparison of merging and meta-analysis as alternative approaches for integrative gene expression analysis.BRAFV600E-Associated Gene Expression Profile: Early Changes in the Transcriptome, Based on a Transgenic Mouse Model of Papillary Thyroid Carcinoma.Comprehensive literature review and statistical considerations for microarray meta-analysisSocial networks help to infer causality in the tumor microenvironment.Monocytes and macrophages, implications for breast cancer migration and stem cell-like activity and treatmentThe T box transcription factor TBX2 promotes epithelial-mesenchymal transition and invasion of normal and malignant breast epithelial cells.Risk-conscious correction of batch effects: maximising information extraction from high-throughput genomic datasetsTargeting of Rac GTPases blocks the spread of intact human breast cancer.MINT: a multivariate integrative method to identify reproducible molecular signatures across independent experiments and platformsGene expression analysis supports tumor threshold over 2.0 cm for T-category breast cancer.Gene expression profile analysis of t1 and t2 breast cancer reveals different activation pathwaysIdentification of novel pathways linking epithelial-to-mesenchymal transition with resistance to HER2-targeted therapy.Microarray Meta-Analysis and Cross-Platform Normalization: Integrative Genomics for Robust Biomarker DiscoveryEffects of sample size on robustness and prediction accuracy of a prognostic gene signature.The Role of Proliferation in Determining Response to Neoadjuvant Chemotherapy in Breast Cancer: A Gene Expression-Based Meta-Analysis.Low PIP4K2B expression in human breast tumors correlates with reduced patient survival: A role for PIP4K2B in the regulation of E-cadherin expression.Differential expression analysis for individual cancer samples based on robust within-sample relative gene expression orderings across multiple profiling platforms.Prospects for molecular staging of non-small-cell lung cancer from genomic alterations.Biomarker detection and categorization in ribonucleic acid sequencing meta-analysis using Bayesian hierarchical models.Study of Meta-analysis strategies for network inference using information-theoretic approaches.Unveiling gene trait relationship by cross-platform meta-analysis on Chinese hamster ovary cell transcriptome.FGFR4 signaling couples to Bim and not Bmf to discriminate subsets of alveolar rhabdomyosarcoma cells.
P2860
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P2860
The removal of multiplicative, systematic bias allows integration of breast cancer gene expression datasets - improving meta-analysis and prediction of prognosis.
description
2008 nî lūn-bûn
@nan
2008 թուականի Սեպտեմբերին հրատարակուած գիտական յօդուած
@hyw
2008 թվականի սեպտեմբերին հրատարակված գիտական հոդված
@hy
2008年の論文
@ja
2008年論文
@yue
2008年論文
@zh-hant
2008年論文
@zh-hk
2008年論文
@zh-mo
2008年論文
@zh-tw
2008年论文
@wuu
name
The removal of multiplicative, ...... s and prediction of prognosis.
@ast
The removal of multiplicative, ...... s and prediction of prognosis.
@en
type
label
The removal of multiplicative, ...... s and prediction of prognosis.
@ast
The removal of multiplicative, ...... s and prediction of prognosis.
@en
prefLabel
The removal of multiplicative, ...... s and prediction of prognosis.
@ast
The removal of multiplicative, ...... s and prediction of prognosis.
@en
P2093
P2860
P356
P1433
P1476
The removal of multiplicative, ...... s and prediction of prognosis.
@en
P2093
Anthony Howell
Graeme J Smethurst
Michal J Okoniewski
Robert B Clarke
Stuart D Pepper
Yvonne Hey
P2860
P2888
P356
10.1186/1755-8794-1-42
P577
2008-09-21T00:00:00Z
P5875
P6179
1034319319