Amanote Research

Amanote Research

    RegisterSign In

Detecting Multivariate Differentially Expressed Genes

BMC Bioinformatics - United Kingdom
doi 10.1186/1471-2105-8-150
Full Text
Open PDF
Abstract

Available in full text

Categories
BiochemistryApplied MathematicsComputer Science ApplicationsStructural BiologyMolecular Biology
Date

May 9, 2007

Authors
Roland NilssonJosé M PeñaJohan BjörkegrenJesper Tegnér
Publisher

Springer Science and Business Media LLC


Related search

Table 2: Differentially Expressed Genes.

English

A Comparison of Statistical Methods for Detecting Differentially Expressed Genes From RNA-seq Data

American Journal of Botany
EvolutionEcologyGeneticsSystematicsPlant ScienceBehavior
2012English

Selecting Differentially Expressed Genes From Microarray Experiments

Biometrics
StatisticsGeneticsProbabilityMolecular BiologyBiochemistryApplied MathematicsMicrobiology ImmunologyBiological SciencesMedicineAgricultural
2003English

PATHOME: An Algorithm for Accurately Detecting Differentially Expressed Subpathways

Oncogene
Cancer ResearchGeneticsMolecular Biology
2014English

Table S2: Differentially Expressed Genes and lncRNAs

English

Discovering Differentially Expressed Genes in Yeast Stress Data

2014English

Using Mixture Models to Detect Differentially Expressed Genes

Australian Journal of Experimental Agriculture
2005English

Figure 2: GO Analysis of Differentially Expressed Genes.

English

Figure 4: Heatmaps of the Differentially Expressed Genes.

English

Amanote Research

Note-taking for researchers

Follow Amanote

© 2025 Amaplex Software S.P.R.L. All rights reserved.

Privacy PolicyRefund Policy