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

Is cross-validation valid for small-sample microarray classification?

Bioinformatics (Oxford, England) - 12 Feb 2004

Braga-Neto Ulisses M, Dougherty Edward R

Abstract excerpt

MOTIVATION: Microarray classification typically possesses two striking attributes: (1) classifier design and error estimation are based on remarkably small samples and (2) cross-validation error estimation is employed in the majority of the papers. Thus, it is necessary to have a quantifiable und...

Read the complete abstract on PubMed

Topics

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

Share to Benchmarking
Share to Computer Simulation
Share to Models, Statistical

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Oligonucleotide Array Sequence Analysis

Share to Pattern Recognition, Automated

Share to Reproducibility of Results

Share to Sample Size

Share to Sensitivity and Specificity

Source

Identifiers

PMID: 14960464
DOI: 10.1093/bioinformatics/btg419

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 1 Sept 2026. Prepared record publication-14960464-2026-09-01.1. Manifest publication-graph-cohort-2026-09-01.1.

Source receipt: PubMed via Helena Literature Corpus, retrieved 1 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 8 closest

Is cross-validation valid for small-sample microarray classification?PMID 14960464Read ↗

Estimating misclassification error with small samples via bootstrap cross-validation.Semantic proximity Read ↗

A comparison of bootstrap methods and an adjusted bootstrap approach for estimating the prediction error in microarray classification.Semantic proximity Read ↗

Small sample issues for microarray-based classification.Semantic proximity Read ↗

Statistical considerations for analysis of microarray experiments.Semantic proximity Read ↗

Assessing stability of gene selection in microarray data analysis.Semantic proximity Read ↗

Support vector machine quantile regression for detecting differentially expressed genes in microarray analysis.Semantic proximity Read ↗

Blood-based transcriptomic classification of lung cancer: a leakage-free nested cross-validation framework with LASSOSemantic proximity Read ↗

Recommendations for Validating Omics Prediction Models: Insights from a Lung Cancer RNA Biomarker StudySemantic proximity Read ↗

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