April 2026 in “International Journal of Engineering Research and Science & Technology” This study reports that an Explainable AI-based hair health prediction system using a novel hybrid model outperformed traditional machine learning methods, achieving high accuracy in predicting key factors and providing personalized recommendations.
18 citations
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January 2020 in “Frontiers in Chemistry” This study developed a deep learning-based method that identified 3,620,516 potential drug-disease associations, suggesting a promising tool for large-scale virtual screening in drug research.
In this study, baseline neutrophil-to-lymphocyte ratio (NLR) was associated with predicting early trichoscopic response in patients undergoing PRP-based treatment for non-scarring alopecia.
61 citations
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June 2022 in “IEEE Journal of Biomedical and Health Informatics” This study introduced a novel deep clustering approach for melanoma detection from dermoscopic images, demonstrating improved performance over existing methods by mitigating class imbalance issues using a center-oriented margin-free triplet loss.
3 citations
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December 2018 in “Meta Gene” This study applied a prediction model based on five SNPs to Russian males with male pattern hair loss, finding a significant association between the AR genomic region and high dihydrotestosterone levels in these patients.
