ArticleiScience2021
SkinBug: an artificial intelligence approach to predict human skin microbiome-mediated metabolism of biotics and xenobiotics.
Article in iScience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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.
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.
Who cites it
11 citing papers in PubMed, 19 citations in OpenAlex.
- Elucidating the Impact of Bacterial Composition and Diversity on Skin Homeostasis Using a Human 3D In Vitro Skin Model.Microorganisms · 2026Article
- Omics-based decoding of molecular and metabolic crosstalk in the skin barrier ecosystem.Cell death and differentiation · 2026Review
- Effect of Thioredoxin Tag, Oxidizing Environment, and Temperature on the Global Metabolome of E. Coli Strains.Applied biochemistry and biotechnology · 2026Article
- Skin in the game: a review of computational models of the skin.BioData mining · 2025Review
- XenoBug: machine learning-based tool to predict pollutant-degrading enzymes from environmental metagenomes.NAR genomics and bioinformatics · 2025Article
- GutBugDB: a web resource to predict the human gut microbiome-mediated biotransformation of biotic and xenobiotic molecules.Gut microbiome (Cambridge, England) · 2025Article
- Evolving approaches to profiling the microbiome in skin disease.Frontiers in immunology · 2023Review
- Application of artificial intelligence approaches to predict the metabolism of xenobiotic molecules by human gut microbiome.Frontiers in microbiology · 2023Review
- SKIOME Project: a curated collection of skin microbiome datasets enriched with study-related metadata.Database : the journal of biological databases and curation · 2022Article
- Exposome and Skin: Part 1. Bibliometric Analysis and Review of the Impact of Exposome Approaches on Dermatology.Dermatology and therapy · 2022Review
- Commensal-Related Changes in the Epidermal Barrier Function Lead to Alterations in the Benzo[mBio · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In addition to being pivotal for the host health, the skin microbiome possesses a large reservoir of metabolic enzymes, which can metabolize molecules (cosmetics, medicines, pollutants, etc.) that form a major part of the skin exposome. Therefore, to predict the complete metabolism of any molecule by skin microbiome, a curated database of metabolic enzymes (1,094,153), reactions, and substrates from ∼900 bacterial species from 19 different skin sites were used to develop "SkinBug." It integrates machine learning, neural networks, and chemoinformatics methods, and displays a multiclass multilabel accuracy of up to 82.4% and binary accuracy of up to 90.0%. SkinBug predicts all possible metabolic reactions and associated enzymes, reaction centers, skin microbiome species harboring the enzyme, and the respective skin sites. Thus, SkinBug will be an indispensable tool to predict xenobiotic/biotic metabolism by skin microbiome and will find applications in exposome and microbiome studies, dermatology, and skin cancer research.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.