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- Classification Framework for Healthy Hairs and Alopecia Areata: A Machine Learning (ML) Approach
- Machine Learning Applications for Drug Repurposing
- Exploring machine learning strategies for single-cell transcriptomic analysis in wound healing
- A Genome-Wide Association Study and Machine-Learning Algorithm Analysis on the Prediction of Facial Phenotypes by Genotypes in Korean Women
- Dermacen Analytica: A Novel Methodology Integrating Multi-Modal Large Language Models with Machine Learning in tele-dermatology
- Machine learning-based steroid metabolome analysis reveals three distinct subtypes of polycystic ovary syndrome and implicates 11-oxygenated androgens as major drivers of metabolic risk
- Machine Learning Guided Discovery of Superoxide Dismutase Nanozymes for Androgenetic Alopecia
- Automated Machine Learning Analysis of Patients With Chronic Skin Disease Using a Medical Smartphone App: Retrospective Study
- Machine learning for nanozyme development in anti‐inflammatory applications
- Early prediction of alopecia areata using machine learning modeling of neuro stress immune signatures from multi datasets
- Diagnosis of Scalp Disorders using Machine Learning and Deep Learning Approach -- A Review
- Machine learning driven multi-omics analysis of the genetic mechanisms behind the double-coat fleece formation in Hetian sheep
- Machine Learning Approach for Predicting Systemic Lupus Erythematosus in Oman-based Cohort
- Machine Learning–Enhanced Pharmacovigilance Assessment of Cardiac Adverse Events Associated with Minoxidil
- Machine Learning–Enhanced Pharmacovigilance Assessment of Cardiac Adverse Events Associated with Minoxidil
- Machine learning applications in androgenetic alopecia: a systematic review
- An Early Hair Loss Detection and Prediction Method Based on Machine Learning
- Identification of Stress-Related Hair Loss and Prevention Through the Application of a KNN Model Based on Machine Learning
- Microbial dysbiosis and its diagnostic potential in androgenetic alopecia: insights from multi-kingdom sequencing and machine learning
- Using machine learning to identify predictors of maternal and infant hair cortisol concentration before and during the COVID-19 pandemic
- Emerging smart microneedle technologies in psoriasis: convergence of nanocarriers, machine learning, and personalized delivery
- A Machine Learning Algorithm Applied to Trichoscopy for Androgenic Alopecia Staging and Severity Assessment
- Editorial: Machine Learning Techniques on Gene Function Prediction Volume II
- Machine learning‐guided multi‐omics suggests iron‐dependent hormonal signaling drives root morphological plasticity in wheat under temperature stress
- Machine Learning--Enhanced Pharmacovigilance Assessment of Cardiac Adverse Events Associated With Minoxidil
- Machine learning predicts scarring progression in lichen planopilaris: A multidimensional model integrating trichoscopy, vitamin D, and diagnostic timelines
- Machine Learning-Driven Optimization of Therapeutic Substance Composition for High-Hardness, Fast-Dissolving Microneedles for Androgenetic Alopecia Treatment.
- LB1040 Machine learning-based predictive model with routine blood work identifies moderate-severe alopecia areata
- Research on Mental Stress Recognition of Depressive Disorders in Patients With Androgenic Alopecia Based on Machine Learning and Fuzzy K-Means Clustering
- A machine learning and network framework to discover new indications for small molecules