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

    Carrieri, AP, Haiminen, N, Maudsley-Barton, S, Gardiner, LJ, Murphy, B, Mayes, AE, Paterson, S, Grimshaw, S, Winn, M, Shand, C, Hadjidoukas, P, Rowe, WPM, Hawkins, S, MacGuire-Flanagan, A, Tazzioli, J, Kenny, JG, Parida, L, Hoptroff, M and Pyzer-Knapp, EO (2021) Explainable AI reveals changes in skin microbiome composition linked to phenotypic differences. Scientific Reports, 11 (1). ISSN 2045-2322

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    Alterations in the human microbiome have been observed in a variety of conditions such as asthma, gingivitis, dermatitis and cancer, and much remains to be learned about the links between the microbiome and human health. The fusion of artificial intelligence with rich microbiome datasets can offer an improved understanding of the microbiome’s role in human health. To gain actionable insights it is essential to consider both the predictive power and the transparency of the models by providing explanations for the predictions. We combine the collection of leg skin microbiome samples from two healthy cohorts of women with the application of an explainable artificial intelligence (EAI) approach that provides accurate predictions of phenotypes with explanations. The explanations are expressed in terms of variations in the relative abundance of key microbes that drive the predictions. We predict skin hydration, subject's age, pre/post-menopausal status and smoking status from the leg skin microbiome. The changes in microbial composition linked to skin hydration can accelerate the development of personalized treatments for healthy skin, while those associated with age may offer insights into the skin aging process. The leg microbiome signatures associated with smoking and menopausal status are consistent with previous findings from oral/respiratory tract microbiomes and vaginal/gut microbiomes respectively. This suggests that easily accessible microbiome samples could be used to investigate health-related phenotypes, offering potential for non-invasive diagnosis and condition monitoring. Our EAI approach sets the stage for new work focused on understanding the complex relationships between microbial communities and phenotypes. Our approach can be applied to predict any condition from microbiome samples and has the potential to accelerate the development of microbiome-based personalized therapeutics and non-invasive diagnostics.

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