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Batch effect removal methods for microarray gene expression data integration: a survey.

Batch effect removal methods for microarray gene expression data integration: a survey.

http://www.wikidata.org/entity/Q34360830

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Development of a Drug-Response Modeling Framework to Identify Cell Line Derived Translational Biomarkers That Can Predict Treatment Outcome to Erlotinib or Sorafenib A reproducible approach to high-throughput biological data acquisition and integration Multi-omics integration accurately predicts cellular state in unexplored conditions for Escherichia coli 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.Comparison of gene expression microarray data with count-based RNA measurements informs microarray interpretation.Between-array normalization for 450K data.Identification of functionally methylated regions based on discriminant analysis through integrating methylation and gene expression data.Discovering transnosological molecular basis of human brain diseases using biclustering analysis of integrated gene expression data.Discretization of gene expression data revised.Combining location-and-scale batch effect adjustment with data cleaning by latent factor adjustment.Single-Patient Molecular Testing with NanoString nCounter Data Using a Reference-Based Strategy for Batch Effect Correction.Removing Batch Effects from Longitudinal Gene Expression - Quantile Normalization Plus ComBat as Best Approach for Microarray Transcriptome Data.A comparative evaluation of data-merging and meta-analysis methods for reconstructing gene-gene interactions ICN: a normalization method for gene expression data considering the over-expression of informative genes.Immune-Signatures for Lung Cancer Diagnostics: Evaluation of Protein Microarray Data Normalization Strategies.Variation of RNA Quality and Quantity Are Major Sources of Batch Effects in Microarray Expression Data.Batch effect confounding leads to strong bias in performance estimates obtained by cross-validation.RRmix: A method for simultaneous batch effect correction and analysis of metabolomics data in the absence of internal standards.Integrative analysis of pathway deregulation in obesity Measuring the effect of inter-study variability on estimating prediction error.Comparison of merging and meta-analysis as alternative approaches for integrative gene expression analysis.Integrated network analysis and logistic regression modeling identify stage-specific genes in Oral Squamous Cell Carcinoma A robust blood gene expression-based prognostic model for castration-resistant prostate cancer BEclear: Batch Effect Detection and Adjustment in DNA Methylation Data.Risk-conscious correction of batch effects: maximising information extraction from high-throughput genomic datasets MINT: a multivariate integrative method to identify reproducible molecular signatures across independent experiments and platforms Higher plasma levels of lysophosphatidylcholine 18:0 are related to a lower risk of common cancers in a prospective metabolomics study FERAL: network-based classifier with application to breast cancer outcome prediction.An individualized prognostic signature for gastric cancer patients treated with 5-Fluorouracil-based chemotherapy and distinct multi-omics characteristics of prognostic groups.An individualized prognostic signature and multi‑omics distinction for early stage hepatocellular carcinoma patients with surgical resection Discriminating cancer-related and cancer-unrelated chemoradiation-response genes for locally advanced rectal cancers.Inhibition of mTOR induces a paused pluripotent state.Sources of high variance between probe signals in Affymetrix short oligonucleotide microarrays.Comparison of methods to identify aberrant expression patterns in individual patients: augmenting our toolkit for precision medicine.The 'omics' of adrenocortical tumours for personalized medicine.Microbial forensics: predicting phenotypic characteristics and environmental conditions from large-scale gene expression profiles.Data aggregation at the level of molecular pathways improves stability of experimental transcriptomic and proteomic data.The E. coli molecular phenotype under different growth conditions.Study of Meta-analysis strategies for network inference using information-theoretic approaches.

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P2860

Batch effect removal methods for microarray gene expression data integration: a survey.

http://www.wikidata.org/entity/Q34360830

description

2012 nî lūn-bûn

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2012 թուականի Յուլիսին հրատարակուած գիտական յօդուած

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2012 թվականի հուլիսին հրատարակված գիտական հոդված

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2012年の論文

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2012年論文

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2012年论文

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Batch effect removal methods for microarray gene expression data integration: a survey.

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Batch effect removal methods for microarray gene expression data integration: a survey.

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Batch effect removal methods for microarray gene expression data integration: a survey.

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Batch effect removal methods for microarray gene expression data integration: a survey.

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Batch effect removal methods for microarray gene expression data integration: a survey.

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Batch effect removal methods for microarray gene expression data integration: a survey.

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P1433

Briefings in Bioinformatics

P1476

Batch effect removal methods for microarray gene expression data integration: a survey.

@en

P2093

Alain Coletta

http://www.w3.org/2001/XMLSchema#string

Ann Nowé

http://www.w3.org/2001/XMLSchema#string

Colin Molter

http://www.w3.org/2001/XMLSchema#string

Cosmin Lazar

http://www.w3.org/2001/XMLSchema#string

David Steenhoff

http://www.w3.org/2001/XMLSchema#string

David Y Weiss-Solís

http://www.w3.org/2001/XMLSchema#string

Hugues Bersini

http://www.w3.org/2001/XMLSchema#string

Jonatan Taminau

http://www.w3.org/2001/XMLSchema#string

Robin Duque

http://www.w3.org/2001/XMLSchema#string

Stijn Meganck

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P2860

Joint analysis of two microarray gene-expression data sets to select lung adenocarcinoma marker genes

Sources of variability and effect of experimental approach on expression profiling data interpretation

Universal Reference RNA as a standard for microarray experiments

Cross-platform analysis of cancer microarray data improves gene expression based classification of phenotypes

Cross-site comparison of gene expression data reveals high similarity.

Sources of variation in Affymetrix microarray experiments.

Harshlight: a "corrective make-up" program for microarray chips.

Gene Expression Omnibus: NCBI gene expression and hybridization array data repository

A comparison of normalization methods for high density oligonucleotide array data based on variance and bias

Exploration, normalization, and summaries of high density oligonucleotide array probe level data

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469-490

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scholarly article

P356

10.1093/BIB/BBS037

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P433

4

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P478

14

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2012-07-31T00:00:00Z

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P698

22851511

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