Key issues in conducting a meta-analysis of gene expression microarray datasets. - Wikidata - awgldk - TriplyDB

Key issues in conducting a meta-analysis of gene expression microarray datasets.

Key issues in conducting a meta-analysis of gene expression microarray datasets.

http://www.wikidata.org/entity/Q33366123

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Systems immunology of human malaria When is hub gene selection better than standard meta-analysis?Identification of common differentially expressed genes in urinary bladder cancer Translational bioinformatics applications in genome medicine Microarray Meta-Analysis Focused on the Response of Genes Involved in Redox Homeostasis to Diverse Abiotic Stresses in Rice Multiplex meta-analysis of RNA expression to identify genes with variants associated with immune dysfunction A crowdsourcing approach for reusing and meta-analyzing gene expression data Large-scale integration of microarray data reveals genes and pathways common to multiple cancer types Molecular classification and novel targets in hepatocellular carcinoma: recent advancements Statistical Methods in Integrative Genomics Identification of reference genes across physiological states for qRT-PCR through microarray meta-analysis yStreX: yeast stress expression database A Systematic Framework for Drug Repositioning from Integrated Omics and Drug Phenotype Profiles Using Pathway-Drug Network Reuse of public genome-wide gene expression data Integrative meta-analysis identifies microRNA-regulated networks in infantile hemangioma Meta-analysis of age-related gene expression profiles identifies common signatures of aging.Drug repositioning in SLE: crowd-sourcing, literature-mining and Big Data analysis.Analyzing illumina gene expression microarray data from different tissues: methodological aspects of data analysis in the metaxpress consortium A data similarity-based strategy for meta-analysis of transcriptional profiles in cancer.INMEX--a web-based tool for integrative meta-analysis of expression data Reconstructing targetable pathways in lung cancer by integrating diverse omics data FUT11 as a potential biomarker of clear cell renal cell carcinoma progression based on meta-analysis of gene expression data MAAMD: a workflow to standardize meta-analyses and comparison of affymetrix microarray data.Comprehensive gene expression meta-analysis of head and neck squamous cell carcinoma microarray data defines a robust survival predictor.omicsNPC: Applying the Non-Parametric Combination Methodology to the Integrative Analysis of Heterogeneous Omics Data.Meta-analysis of differentially expressed genes in osteosarcoma based on gene expression data.Type I interferon related genes are common genes on the early stage after vaccination by meta-analysis of microarray data.Identification of Commonly Dysregulated Genes in Non-small-cell Lung Cancer by Integrated Analysis of Microarray Data and qRT-PCR Validation.Identification of commonly dysregulated genes in colorectal cancer by integrating analysis of RNA-Seq data and qRT-PCR validation.NetworkAnalyst for statistical, visual and network-based meta-analysis of gene expression data.Rethinking Meta-Analysis: Applications for Air Pollution Data and Beyond.Meta-Analysis of Placental Transcriptome Data Identifies a Novel Molecular Pathway Related to Preeclampsia A novel tissue-specific meta-analysis approach for gene expression predictions, initiated with a mammalian gene expression testis database.AnyExpress: integrated toolkit for analysis of cross-platform gene expression data using a fast interval matching algorithm.NABIC Microarray: an integrated database of high throughput data for gene expression profiles.Meta-analysis of transcriptome data identifies a novel 5-gene pancreatic adenocarcinoma classifier.A comparative evaluation of data-merging and meta-analysis methods for reconstructing gene-gene interactions Integrative clustering of multi-level omics data for disease subtype discovery using sequential double regularization.Hippocampal gene expression meta-analysis identifies aging and age-associated spatial learning impairment (ASLI) genes and pathways.Benchmarking Sepsis Gene Expression Diagnostics Using Public Data.

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Key issues in conducting a meta-analysis of gene expression microarray datasets.

http://www.wikidata.org/entity/Q33366123

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2008 թվականի սեպտեմբերին հրատարակված գիտական հոդված

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Key issues in conducting a meta-analysis of gene expression microarray datasets.

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Key issues in conducting a meta-analysis of gene expression microarray datasets.

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Key issues in conducting a meta-analysis of gene expression microarray datasets.

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Key issues in conducting a meta-analysis of gene expression microarray datasets.

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Key issues in conducting a meta-analysis of gene expression microarray datasets.

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Key issues in conducting a meta-analysis of gene expression microarray datasets.

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Key issues in conducting a meta-analysis of gene expression microarray datasets.

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Bioconductor: open software development for computational biology and bioinformatics

Forest plots: trying to see the wood and the trees

The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements

Submission of microarray data to public repositories

Mistaken identifiers: gene name errors can be introduced inadvertently when using Excel in bioinformatics

Redefinition of Affymetrix probe sets by sequence overlap with cDNA microarray probes reduces cross-platform inconsistencies in cancer-associated gene expression measurements.

Previously unidentified changes in renal cell carcinoma gene expression identified by parametric analysis of microarray data

RESOURCERER: a database for annotating and linking microarray resources within and across species

Comparing independent microarray studies: the case of human embryonic stem cells

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

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