Back to search

Article

Machine learning applied to atopic dermatitis transcriptome reveals distinct therapy-dependent modification of the keratinocyte immunophenotype

2019-12-19

Abstract excerpt

<h4>Background</h4> Atopic dermatitis (AD) arises from a complex interaction between an impaired epidermal barrier, environmental exposures, and the infiltration of Th1/Th2/Th17/Th22 T cells. Transcriptomic analysis has advanced understanding of gene expression in cells and tissues. However, molecular quantitation of cytokine transcripts does not predict the importance of a specific pathway in AD or cellular respo...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work
41134a7a-1927-58eb-9c51-d4fe906aa6b6
DOI
10.1101/2019.12.14.19014977
Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest
Interactive article relationship graphSelect a related publication card to move it into the centre and load its closest explainable connections. Solid lines are source-backed structured connections. Dashed lines are semantic discovery signals and are not scientific evidence.
Machine learning applied to atopic dermatitis transcriptome reveals distinct therapy-dependent modification of the keratinocyte immunophenotypeDOI 10.1101/2019.12.14.19014977
Machine learning applied to atopic dermatitis transcriptome reveals distinct therapy‐dependent modification of the keratinocyte immunophenotype*Semantic proximity
An unbiased tissue transcriptome analysis identifies potential markers for skin phenotypes and therapeutic responses in atopic dermatitisSemantic proximity
Transcriptomic Subtyping of Atopic Dermatitis Reveals Distinct Drug Response SignaturesSemantic proximity
Comprehensive Transcriptomic Analysis of Atopic Dermatitis Patients Documents the Spectrum of Molecular Abnormalities and the Response to TreatmentSemantic proximity
Multifaceted analysis of cross-tissue transcriptomes reveals phenotype-endotype associations in atopic dermatitis.Semantic proximity
Integrative network analysis suggests prioritised drugs for atopic dermatitisSemantic proximity
Meta-Analyzed Atopic Dermatitis Transcriptome (MAADT) is strongly correlated with disease activity, and consistent with therapeutic effectsSemantic proximity
Integrative network analysis suggests prioritised drugs for atopic dermatitis.Semantic proximity
IL-24 and S100A2 as Novel Immune Drivers for Therapeutic Targeting and Precision Subtyping in Atopic DermatitisSemantic proximity
Multifaceted analysis of cross-tissue, cross-sectional and longitudinal transcriptomes reveals phenotype–endotype associations in atopic dermatitis.Semantic proximity
Single-cell transcriptomic mendelian randomization and co-localization reveal immune-mediated regulatory mechanisms and drug targets in atopic dermatitis.Semantic proximity
Deciphering Molecular Underpinnings and Therapeutic Avenues for Eczema and Atopic Dermatitis through Integrated Omics and Immunohistochemical AnalysisSemantic proximity
Select a neighboring publication to make it the new centre.