133 citations
,
February 2017 in “PLoS Genetics” In this study, researchers used genetic data from over 52,000 men to identify over 250 genetic loci associated with severe hair loss and developed a predictive algorithm for determining hair loss risk.
37 citations
,
October 2015 in “European Journal of Human Genetics” This study found that a genetic model using SNPs can predict early-onset male-pattern baldness with moderate accuracy, which may assist in decisions about interventions.
5 citations
,
August 2016 in “bioRxiv (Cold Spring Harbor Laboratory)” In this study, researchers identified over 250 new genetic loci linked to severe male pattern baldness, and developed a prediction algorithm that could accurately differentiate between those with severe and no hair loss.
3 citations
,
November 2022 in “European Journal of Human Genetics” This study developed new genetic prediction models for male pattern baldness with improved accuracy by utilizing a large set of markers and independent datasets, making them the most reliable available for this trait.
July 2026 in “International Journal of Advanced Research in Science Communication and Technology” In this study, the BaldGraphFormer framework, integrating visual and clinical data, outperformed unimodal baselines in early-stage androgenetic alopecia detection, achieving an F1-score of 97.62% and macro-average AUC of 0.992, suggesting its potential to support dermatological decision-making and early intervention.