Evidence map›Paper›PMID 35486723›Full record

ArticleScience advances2022

Machine learning reveals distinct gene signature profiles in lesional and nonlesional regions of inflammatory skin diseases.

Brittany A Martínez, Sneha Shrotri, Kathryn M Kingsmore, Prathyusha Bachali, Amrie C Grammer, Peter E Lipsky

Open access · goldAbstract read
In one paragraph

Article in Science advances, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.

0numbers the graph read from it
0cells of the map it votes in
32citing papers in PubMed
8.9field-weighted citation impact, top 2% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

32 citing papers in PubMed, 50 citations in OpenAlex.

  1. Current research landscape and future prospects of in silico modeling approaches for atopic dermatitis.JID innovations : skin science from molecules to population health · 2026
    Review
  2. Vitiligo Information Resource database v3.NPJ systems biology and applications · 2026
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  10. Review
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  12. Transcriptomic Analysis Identifies Disease Severity and Therapeutic Response in Psoriasis.JID innovations : skin science from molecules to population health · 2025
    Article
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  14. Article
  15. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 1 institution in 1 country.

Brittany A MartínezAMPEL BioSolutions, LLC and the RILITE Research Institute, Charlottesville, VA, USA.ORCID 0000-0001-5594-2618
Sneha ShrotriAMPEL BioSolutions, LLC and the RILITE Research Institute, Charlottesville, VA, USA.ORCID 0000-0001-8528-5313
Kathryn M KingsmoreAMPEL BioSolutions, LLC and the RILITE Research Institute, Charlottesville, VA, USA.ORCID 0000-0002-9923-4709
Prathyusha BachaliAMPEL BioSolutions, LLC and the RILITE Research Institute, Charlottesville, VA, USA.ORCID 0000-0001-7304-1987
Amrie C GrammerAMPEL BioSolutions, LLC and the RILITE Research Institute, Charlottesville, VA, USA.ORCID 0000-0001-7142-2284
Peter E LipskyAMPEL BioSolutions, LLC and the RILITE Research Institute, Charlottesville, VA, USA.ORCID 0000-0002-9287-1676
Ampel BioSolutions (United States) · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Analysis of gene expression from cutaneous lupus erythematosus, psoriasis, atopic dermatitis, and systemic sclerosis using gene set variation analysis (GSVA) revealed that lesional samples from each condition had unique features, but all four diseases displayed common enrichment in multiple inflammatory signatures. These findings were confirmed by both classification and regression tree analysis and machine learning (ML) models. Nonlesional samples from each disease also differed from normal samples and each other by ML. Notably, the features used in classification of nonlesional disease were more distinct than their lesional counterparts, and GSVA confirmed unique features of nonlesional disease. These data show that lesional and nonlesional skin samples from inflammatory skin diseases have unique profiles of gene expression abnormalities, especially in nonlesional skin, and suggest a model in which disease-specific abnormalities in "prelesional" skin may permit environmental stimuli to trigger inflammatory responses leading to both the unique and shared manifestations of each disease.

Indexed as

Dermatitis, AtopicPsoriasisHumansMachine LearningSkin

Identifiers

PMID35486723
PMCPMC9054015
OpenAlexW4225120914

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.