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This study developed a leakage-resistant machine learning framework for classifying hair loss types, emphasizing transparency through explainable AI. Among tested models, Extreme Gradient Boosting excelled, achieving high accuracy and stability on both cross-validation and holdout datasets.

This study found statistically significant patterns in hair morphology and ultrastructure linked to biogeographic ancestry among European, African, and East Asian populations.

In this study, trichoscopy was found to be an effective noninvasive tool for diagnosing female pattern hair loss at early stages, potentially eliminating the need for scalp biopsies.

This study found that experienced Australian prescribers of hormone therapy for transgender adults often follow international guidelines but highlight the need for better training and specific local guidelines.

This study found that DrugNet, a network-based prioritization method, effectively improves drug repositioning tasks, achieving high performance in validation tests and clinical trial comparisons.