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

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

Statistics in medicine - 20 Dec 2007

Jiang Wenyu, Simon Richard

Abstract excerpt

This paper first provides a critical review on some existing methods for estimating the prediction error in classifying microarray data where the number of genes greatly exceeds the number of specimens. Special attention is given to the bootstrap-related methods. When the sample size n is small,...

Read the complete abstract on PubMed

Topics

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

Share to Artificial Intelligence
Share to Bias
Share to Confidence Intervals

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Diagnosis, Computer-Assisted

Share to Discriminant Analysis

Share to Lymphoma

Share to Sample Size

Share to Sampling Studies

Source

Identifiers

PMID: 17624926
DOI: 10.1002/sim.2968

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 4 Sept 2026. Prepared record publication-17624926-2026-09-04.2. 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

A comparison of bootstrap methods and an adjusted bootstrap approach for estimating the prediction error in microarray classification.PMID 17624926Read ↗

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

Is cross-validation valid for small-sample microarray classification?Semantic proximity Read ↗

Empirical Bayes screening of many p-values with applications to microarray studies.Semantic proximity Read ↗

An empirical bayes adjustment to increase the sensitivity of detecting differentially expressed genes in microarray experiments.Semantic proximity Read ↗

A comprehensive simulation study on classification of RNA-Seq dataSemantic proximity Read ↗

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

Empirical study of supervised gene screening.Semantic proximity Read ↗

Local-pooled-error test for identifying differentially expressed genes with a small number of replicated microarrays.Semantic proximity Read ↗

WMW-A: Rank-based two-sample independent test for smallsample sizes through an auxiliary sampleSemantic proximity Read ↗

Cross phenotype normalization of microarray data.Semantic proximity Read ↗

Advanced significance analysis of microarray data based on weighted resampling: a comparative study and application to gene deletions in Mycobacterium bovis.Semantic proximity Read ↗

A bootstrap-based regression method for comprehensive discovery of differential gene expressions: an application to the osteoporosis study.Semantic proximity Read ↗

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