research A Gender Hypothesis of sex disparities in adverse drug events
Gendered social factors, not just biology, contribute to sex differences in adverse drug events.
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research Classification Framework for Healthy Hairs and Alopecia Areata: A Machine Learning (ML) Approach
Machine learning can accurately identify Alopecia Areata, aiding in early detection and treatment of this hair loss condition.
research Machine Learning Applications for Drug Repurposing
Machine learning helps find new uses for existing drugs, improving healthcare.
research Identification of Hypoxia and Mitochondrial-related Gene Signature and Prediction of Prognostic Model in Lung Adenocarcinoma
A gene-based model predicts lung adenocarcinoma outcomes and helps guide treatment decisions.
research Testing the impact of trait prevalence priors in Bayesian-based genetic prediction modeling of human appearance traits
Using trait prevalence priors in genetic prediction models for appearance traits is currently impractical due to limited knowledge and potential accuracy issues.
research Construction of regulatory network for alopecia areata progression and identification of immune monitoring genes based on multiple machine-learning algorithms
Researchers found four genes that could help diagnose severe alopecia areata early.
research Hair and Scalp Disease Detection using Machine Learning and Image Processing
The machine learning model accurately detected hair loss and scalp diseases using processed images.
research Hair Tone Estimation at Roots via Imaging Device with Embedded Deep Learning
The device accurately estimates natural hair color at the roots in real time.
research Quantitative analysis and development of alopecia areata classification frameworks
A new algorithm effectively classifies Alopecia Areata, aiding early detection and treatment.
research Pharmacophore and Atom Based 3D QSAR Studies on the Novel 5-Alpha-Reductase Inhibitors
The developed model can predict effective 5-alpha-reductase enzyme inhibitors.
research A novel eco-friendly Acinetobacter strain A1-4-2 for bioremediation of aquatic pollutants
Acinetobacter strain A1-4-2 can safely clean water pollutants.
research AI-driven biomaterial design: an intelligent closed loop from reverse design to biological response
AI improves biomaterial design by making it faster, cheaper, and more effective for personalized medicine.
research Deep Learning Approaches for Hair Disease Classification: A Comparative Analysis of MobileNetV2 and VGG19 Architectures
VGG19 is more accurate, but MobileNetV2 is faster and uses fewer resources.
research Mendelian randomization analysis of the causal relationship between low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, and androgenic alopecia
Higher LDL cholesterol may increase the risk of hair loss, while HDL cholesterol does not.
research Modeling Oxidative Stress-Linked Telogen Effluvium Using Monte Carlo Simulation of Published Trichoscopy Norms and Cannabis Exposure Distributions
Higher cannabis exposure may lead to increased hair loss.
research Correction to ‘Retinoic acid drives hair follicle stem cell activation via Wnt/β‐catenin signalling in androgenetic alopecia’
The correction does not change the study's main findings.
research Modeling Oxidative Stress–Linked Telogen Effluvium: A Monte Carlo Simulation Using Published Trichoscopy Norms and Cannabis Exposure Distributions
Higher cannabis use may be linked to more severe hair loss.
research Modeling Oxidative Stress–Linked Telogen Effluvium: A Monte Carlo Simulation Using Published Trichoscopy Norms and Cannabis Exposure Distributions
Higher cannabis exposure may worsen hair loss.
research An Early Hair Loss Detection and Prediction Method Based on Machine Learning
Machine learning can accurately predict hair loss early, improving treatment options.
research Comparison of Linear Regression, Decision Tree Regression, and Random Forest Regression Algorithms in Predicting Baldness Risk
Random Forest Regression is best for predicting baldness risk.
research Rapid Screening of Anticoagulation Compounds for Biological Target-Associated Adverse Effects Using a Deep-Learning Framework in the Management of Atrial Fibrillation
The framework helps predict adverse effects of blood thinners, improving drug selection for atrial fibrillation.
research Severity Prediction of Hair Loss in Alopecia Areata with Hybrid Deep Learning Architecture
The model accurately predicts hair loss severity in alopecia areata.
research Integrated multiple machine learning and Mendelian randomization reveal LTF gene as a prognostic biomarker for nonspecific orbital inflammation
The LTF gene may help predict and manage nonspecific orbital inflammation.
research A Data-Driven Approach to Polycystic Ovary Syndrome Diagnosis: Evaluating Machine Learning Models
Machine learning can improve early and accurate detection of PCOS.
research A Fuzzy Logic-Based Computational Framework for Precision Triage in Androgenetic Alopecia: A Simulated Biomarker-Driven Approach
The TPAP method effectively categorizes androgenetic alopecia patients with high accuracy, but needs real-world validation.
research CNN-KNN Model for Assessing Hair Health in Telogen Effluvium
The model accurately predicts hair breakage in Telogen Effluvium, aiding early detection and treatment.
research Deep Learning-Powered Hair Disease Diagnosis: A ResNet50 Approach for Scalable and Accurate Classification
The model accurately diagnoses hair diseases with 95% accuracy using deep learning.
research A Hybrid Transfer Learning Approach Using Obesity Data for Predicting Cardiovascular Diseases Incorporating Lifestyle Factors
Adding obesity data to machine learning models improves heart disease prediction accuracy.
research Optimized VGG19 Architecture for Precise and Efficient Multi-Class Hair Disease Classification
The optimized VGG19 model accurately classifies hair diseases with 98.64% accuracy.
research Advancing Hair Disease Diagnostics: A Deep Learning Approach Using Inception-ResNet v2 for Multi-Class Classification
The model accurately identifies hair diseases using deep learning.