Evidence map›Paper›PMID 42614600›Full record

ReviewJID innovations : skin science from molecules to population health2026

Current research landscape and future prospects of in silico modeling approaches for atopic dermatitis.

Hiu Lam Athena Wu, Pierre Le Floch, Ariane Duverdier, Alan D Irvine, Sandrine Dubrac, Reiko J Tanaka

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in JID innovations : skin science from molecules to population health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Hiu Lam Athena WuDepartment of Bioengineering, Imperial College London, London, United Kingdom.
Pierre Le FlochDepartment of Bioengineering, Imperial College London, London, United Kingdom.
Ariane DuverdierDepartment of Bioengineering, Imperial College London, London, United Kingdom.
Alan D IrvineClinical Medicine, Trinity College Dublin, Dublin, Ireland.
Sandrine DubracDepartment of Dermatology, Venereology and Allergology, Medical University of Innsbruck, Innsbruck, Austria.
Reiko J TanakaDepartment of Bioengineering, Imperial College London, London, United Kingdom.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Atopic dermatitis (AD) is a chronic, multifactorial inflammatory skin disease with a complex, heterogeneous pathogenesis. Understanding its mechanisms, stratifying patients into biologically relevant endotypes, and predicting treatment responses remain challenging if we use empirical approaches alone. In silico approaches, including mathematical modeling, statistical and machine learning methods, enable the dissection of molecular and cellular interactions, the identification of key clinical and biological drivers, and the extraction of meaningful insights from high-dimensional, noisy datasets, while preserving a systems-level perspective. This review summarizes recent advancements in in silico approaches for AD and outlines strategies to enhance their translational and clinical utility in AD research.

Indexed as

3RAtopic dermatitisIn silico modelsMachine learning

Identifiers

PMID42614600
PMCPMC13482602

What OpenQuestion holds

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Registered trials

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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.