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Paul Delmar

dblp:41/2860 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2005
—ORCID · unresolved

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Bioinformatics and computational biology · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › gene expression analysis
differential expression analysis
0.112005
VarMixt: efficient variance modelling for the differential analysis of replicated gene expression data · Bioinform. 2005
Bioinformatics and computational biology
gene expression analysis
0.112005
VarMixt: efficient variance modelling for the differential analysis of replicated gene expression data · Bioinform. 2005
Bioinformatics and computational biology › gene expression analysis
microarray data analysis
0.112005
VarMixt: efficient variance modelling for the differential analysis of replicated gene expression data · Bioinform. 2005

Methods — techniques the papers use, named apart from their topics

mixture model · 0.1false discovery rate control · 0.1
YearPublicationVenuePosition
2005 VarMixt: efficient variance modelling for the differential analysis of replicated gene expression data
abstract
MOTIVATION: Identifying differentially regulated genes in experiments comparing two experimental conditions is often a key step in the microarray data analysis process. Many different approaches and methodological developments have been put forward, yet the question remains open. RESULTS: Varmixt is a powerful and efficient novel methodology for this task. It is based on a flexible and realistic variance modelling strategy. It compares favourably with other popular techniques (standard t-test, SAM and Cyber-T). The relevance of the approach is demonstrated with real-world and simulated datasets. The analysis strategy was successfully applied to both a 'two-colour' cDNA microarray and an Affymetrix Genechip. Strong control of false positive and false negative rates is proven in large simulation studies. AVAILABILITY: The R package is freely available at http://www.inapg.inra.fr/ens_rech/mathinfo/recherche/mathematique/outil.html CONTACT: [email protected] SUPPLEMENTARY INFORMATION: http://www.inapg.inra.fr/ens_rech/mathinfo/recherche/mathematique/outil.html.
Paul Delmar, Stéphane Robin, Jean-Jacques Daudin
Bioinform.1