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

Microarrays: how many do you need?

Journal of computational biology : a journal of computational molecular cell biology - 1 Jan 2003

Zien Alexander, Fluck Juliane, Zimmer Ralf, Lengauer Thomas

Abstract excerpt

We estimate the number of microarrays that is required in order to gain reliable results from a common type of study: the pairwise comparison of different classes of samples. We show that current knowledge allows for the construction of models that look realistic with respect to searches for indi...

Read the complete abstract on PubMed

Topics

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

Share to Computational Biology
Share to Data Interpretation, Statistical
Share to Gene Expression Profiling

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Models, Genetic

Share to Oligonucleotide Array Sequence Analysis

Share to Research Design

Share to Selection Bias

Share to Statistics, Nonparametric

Source

Identifiers

PMID: 12935350
DOI: 10.1089/10665270360688246

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 4 Sept 2026. Prepared record publication-12935350-2026-09-04.2. Manifest publication-graph-cohort-2026-09-04.2.

Source receipt: PubMed via Helena Literature Corpus, retrieved 4 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Microarrays: how many do you need?PMID 12935350Read ↗

The magic of microarrays.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 ↗

[Multiple comparison procedures: principles, limits. Applications to microarray phenotype-genotype analysis].Semantic proximity Read ↗

There is no silver bullet--a guide to low-level data transforms and normalisation methods for microarray data.Semantic proximity Read ↗

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

Trends in microarray analysis.Semantic proximity Read ↗

Methods of Analysis and Meta-Analysis for Identifying Differentially Expressed Genes.Semantic proximity Read ↗

An introduction to arrays.Semantic proximity Read ↗

Microarray results: how accurate are they?Semantic proximity Read ↗

Non-parametric, hypothesis-based analysis of microarrays for comparison of several phenotypes.Semantic proximity Read ↗

Biases induced by pooling samples in microarray experiments.Semantic proximity Read ↗

VarMixt: efficient variance modelling for the differential analysis of replicated gene expression data.Semantic proximity Read ↗

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