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, pattern recognition and exploratory data analysis.

Statistics in medicine - 15 Jun 2003

Mertens Bart J A

Abstract excerpt

We explore the application of principal component based approaches to pattern recognition in microarray analysis. A comparative assessment is presented, based on predictive evaluation, following a review of some key methodology. On the basis of these results, we select principal component based linear discrimination for a further in-depth analysis. We are particularly interested in studying the use of principal...

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 Genetic Variation
Share to Leukemia

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Netherlands

Share to Oligonucleotide Array Sequence Analysis

Share to Pattern Recognition, Automated

Share to Probability

Share to Statistics as Topic

Source

Identifiers

PMID: 12754722
DOI: 10.1002/sim.1364

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-08.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Microarrays, pattern recognition and exploratory data analysis.PMID 12754722Read ↗

Principal component analysis based methods in bioinformatics studies.Semantic proximity Read ↗

An unsupervised approach to identify molecular phenotypic components influencing breast cancer features.Semantic proximity Read ↗

Multivariate analysis of complex gene expression and clinical phenotypes with genetic marker data.Semantic proximity Read ↗

Principal component analysis-based unsupervised feature extraction applied to single-cell gene expression analysis <sup>1</sup>Semantic proximity Read ↗

Multiparametric Analysis of Screening Data: Growing Beyond the Single Dimension to Infinity and Beyond.Semantic proximity Read ↗

Improve survival prediction using principal components of gene expression data.Semantic proximity Read ↗

So you think you can PLS-DA?Semantic proximity Read ↗

Integration of gene expression and gene copy number variations with independent component analysis.Semantic proximity Read ↗

Detecting differential gene expression with a semiparametric hierarchical mixture method.Semantic proximity Read ↗

Replication, variation and normalisation in microarray experiments.Semantic proximity Read ↗

Dimensionality reduction methods for microarray cancer data using prior knowledgeSemantic proximity Read ↗

Separation of the largest eigenvalues in eigenanalysis of genotype data from discrete subpopulations.Semantic proximity Read ↗

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