May 2026 in “International Journal of Drug Delivery Technology” This study reports that using machine learning models, particularly XGBoost and Random Forest, can accurately predict PCOS phenotypes based on non-invasive data, with cycle length as the most significant predictor.
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March 2022 in “Clinical Cosmetic and Investigational Dermatology” This study proposed a model that accurately predicts skin condition using genotype information and machine learning, suggesting potential for creating customized cosmetics.
Results are not reported in this abstract, which notes that while Janus kinase inhibitors like baricitinib show therapeutic benefits for alopecia areata, the mechanisms and reliable predictors of response remain unclear.
July 2026 in “Scientific Reports” This study highlights the importance of imputation performance studies for phenotype-associated SNPs in forensics, showing that imputation improves phenotype prediction despite higher error rates for phenotypic SNPs compared to random SNPs.
January 2026 in “Preprints.org” In this study, researchers identified four novel variants in the FGF5 gene associated with the long-haired phenotype in dogs, suggesting additional unexplored genetic factors contribute to this trait beyond the known Lh1-Lh5 alleles.
