Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Methods of Analysis and Meta-Analysis for Identifying Differentially Expressed Genes.

Methods in molecular biology (Clifton, N.J.) - 1 Jan 2018

Kontou Panagiota I, Pavlopoulou Athanasia, Bagos Pantelis G

Abstract excerpt

Microarray approaches are widely used high-throughput techniques to assess simultaneously the expression of thousands of genes under certain conditions and study the effects of certain treatments, diseases, and developmental stages. The traditional way to perform such experiments is to design oligonucleotide hybridization probes that correspond to specific genes and then measure the expression of the genes in...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Gene Expression Profiling
Share to Gene Expression Regulation

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Genetic Association Studies

Share to Genetic Predisposition to Disease

Share to Models, Genetic

Share to Models, Statistical

Share to Transcriptome

Source

Identifiers

PMID: 29876898
DOI: 10.1007/978-1-4939-7868-7_12

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 3 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Methods of Analysis and Meta-Analysis for Identifying Differentially Expressed Genes.PMID 29876898Read ↗

Available software for meta-analyses of genome-wide expression studiesSemantic proximity Read ↗

Designing the database for microarray experiments metadataSemantic proximity Read ↗

Identification of suitable genes contributes to lung adenocarcinoma clustering by multiple meta-analysis methods.Semantic proximity Read ↗

New applications of microarray data analysis: integrating genetics with 'Omics'. Organized by the Cambridge Healthtech Institute, 15-17 August 2007, Washington DC, USA.Semantic proximity Read ↗

Trends in microarray analysis.Semantic proximity Read ↗

Introduction to statistical methods for microRNA analysis.Semantic proximity Read ↗

GeneANOVA--gene expression analysis of variance.Semantic proximity Read ↗

Gene Expression Network Analysis (GENA) – A Biostatistical Method to Unravel Intricate Relationships Between GenesSemantic proximity Read ↗

Microarray data analysis: a hierarchical T-test to handle heteroscedasticity.Semantic proximity Read ↗

GeNNet: An Integrated Platform for Unifying Scientific Workflow Management and Graph Databases for Transcriptome Data AnalysisSemantic proximity Read ↗

Extracting the Strongest Signals from Omics Data: Differentially Expressed Pathways and Beyond.Semantic proximity Read ↗

Using ANOVA to analyze microarray data.Semantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.