Machine Learning in Personalized Skin Care: A Simulation Scheme for Pattern Recognition in Skin Condition Genome-wide Association Studies

Abstract

Personalized medicine is becoming of increasing importance in the study of psoriasis and atopic dermatitis (AD). Because current treatments only target symptoms, early intervention and personalized medicine have a pivotal role in improved health outcomes. To explore this potential, this study investigates the use of direct-to-consumer (DTC) genetic data in devising machine learning models that can pinpoint signatures salient to psoriasis and AD. The study simulates high-dimensional datasets derived from the HapMap 3 and 1000 Genomes Project cohorts (561K and 497K loci, respectively, that act as features). The simulation scheme splits subjects into cases and controls, where randomly selected variants associated with the target phenotypes are introduced into the cases. Unsupervised learning (UMAP) and eight supervised learning techniques are applied to each of the simulated datasets. Our findings suggest that the parametric models tested (SVM, LASSO, and RIDGE) exhibit the best predictive power on the simulated datasets while also yielding high retrieval rates for signatures associated with the target phenotypes.