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Explainable AI reveals changes in skin microbiome composition linked to phenotypic differences.

Anna Paola Carrieri1, Niina Haiminen2, Sean Maudsley-Barton3,4

  • 1The Hartree Centre, Sci-Tech Daresbury, IBM Research, Daresbury, WA4 4AD, UK. acarrieri@uk.ibm.com.

Scientific Reports
|February 26, 2021
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Summary
This summary is machine-generated.

Explainable AI and microbiome analysis can predict health phenotypes from skin samples. This approach links microbial changes to skin hydration, aging, and smoking, paving the way for non-invasive diagnostics.

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Area of Science:

  • Microbiome research
  • Artificial Intelligence
  • Human Health

Background:

  • Human microbiome alterations are linked to various health conditions.
  • Understanding the microbiome's role in health requires integrating AI with microbiome data.
  • Explainable AI (EAI) is crucial for transparent and actionable insights from microbiome predictions.

Purpose of the Study:

  • To apply an EAI approach to leg skin microbiome samples for phenotype prediction.
  • To identify key microbial variations driving these predictions.
  • To explore the potential of skin microbiome for non-invasive health monitoring.

Main Methods:

  • Collected leg skin microbiome samples from two healthy female cohorts.
  • Utilized an explainable artificial intelligence (EAI) model for analysis.
  • Predicted phenotypes including skin hydration, age, menopausal status, and smoking status.

Main Results:

  • Accurate predictions of phenotypes were achieved using the EAI model.
  • Explanations were provided based on the relative abundance of key microbes.
  • Microbiome signatures correlated with skin hydration, age, smoking, and menopausal status.

Conclusions:

  • Leg skin microbiome analysis using EAI can predict various health-related phenotypes.
  • Findings suggest potential for non-invasive diagnosis and monitoring of health conditions.
  • This approach advances the understanding of microbiome-phenotype relationships and personalized therapeutics.