2 citations
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January 2024 In this study, researchers proposed a deep learning approach combining genetic, hormonal, scalp health, and lifestyle data to predict hair loss, employing CNNs for image analysis and RNNs for modeling data over time, although specific results are not reported.
3 citations
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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.
August 2024 in “Archives of Dermatological Research” Certain genetic variants and pathways are linked to hair loss.
7 citations
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July 2018 in “Journal of Investigative Dermatology” This article reviews the genetic research on male androgenetic alopecia, highlighting that over 300 associated risk variants have been identified, with unclear mechanisms of action.
2 citations
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July 1994 in “Journal of Dermatological Science” This study found that a laboratory model using nude mice can produce human hair follicles with amino acid compositions resembling both normal and trichothiodystrophy-affected human scalp hair over extended periods.