research Classification Framework for Healthy Hair and Alopecia Areata: A Machine Learning Approach
Machine learning can accurately identify Alopecia Areata, aiding in early detection and treatment of this hair loss condition.
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30 / 1000+ results research Machine Learning Guided Discovery of Superoxide Dismutase Nanozymes for Androgenetic Alopecia
Machine learning identified promising nanozymes for treating hair loss.
research Automated Machine Learning Analysis of Patients With Chronic Skin Disease Using a Medical Smartphone App: Retrospective Study
Machine learning can predict symptoms and quality of life in chronic skin disease patients using smartphone app data, and shows that app use varies with patient characteristics.
research Machine Learning Approach for Predicting Systemic Lupus Erythematosus in Oman-Based Cohort
The machine learning model accurately predicts Systemic Lupus Erythematosus in Omani patients.
research A Machine Learning Algorithm Applied to Trichoscopy for Androgenic Alopecia Staging and Severity Assessment
The machine learning model effectively assesses the severity of hair loss and could help dermatologists with treatment decisions.
research Editorial: Machine Learning Techniques on Gene Function Prediction Volume II
Machine learning is effective in predicting gene functions and their relationships with diseases.
research Machine Learning-Based Steroid Metabolome Analysis in Women With Polycystic Ovary Syndrome Reveals Three Distinct Androgen Excess Subtypes With Different Metabolic Risk Profiles
Machine learning identified three unique subtypes of androgen excess in women with PCOS, each with different metabolic risks.
research Research on Mental Stress Recognition of Depressive Disorders in Patients with Androgenic Alopecia Based on Machine Learning and Fuzzy K-Means Clustering
The FAW-FS algorithm improves depression recognition, and psychological interventions help AGA patients' mental health.
research A Machine Learning and Network Framework to Discover New Indications for Small Molecules
A machine learning model called CATNIP can predict new uses for existing drugs, like using antidepressants for Parkinson's disease and a thyroid cancer drug for diabetes.
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 A Machine Learning-Based Scalp Hair Inspection and Diagnosis System for Scalp Health
The system can automatically identify different hair and scalp conditions using machine learning.
research A Machine Learning and Network Framework to Discover New Indications for Small Molecules
The model successfully predicted new uses for existing drugs, like using certain hormonal and heart medications for respiratory and Parkinson's diseases, and a cancer drug for diabetes.
research Comparative Analysis of Machine Learning Algorithms in Diagnosis of Polycystic Ovarian Syndrome
The Random Forest algorithm was the most accurate at diagnosing Polycystic Ovarian Syndrome.
research Drug Repurposing Against KRAS Mutant G12C: A Machine Learning, Molecular Docking, and Molecular Dynamics Study
Afatinib, neratinib, and zanubrutinib could be effective against KRASG12C-mutant tumors.
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 Identification of Ferroptosis-Related Biomarkers in Alopecia Areata Through Machine Learning
Four genes are potential markers for hair loss condition alopecia areata, linked to a specific type of cell death.
research Enhanced Hair Disease Classification Using Deep Learning
The model accurately classifies hair conditions with 97% accuracy.
research Deep Review on Alopecia Areata Diagnosis for Hair Loss-Related Autoimmune Disorder
Machine learning and deep learning can effectively diagnose alopecia areata.
research Predictive Modeling of Patient Response to JAK/STAT Inhibitors and Dynamic Patient-Matching
Machine learning can predict how well patients with alopecia areata will respond to certain treatments.
research An Approach to Detect Alopecia Areata Hair Disease Using Deep Learning
Deep learning can accurately detect Alopecia Areata with up to 98.3% accuracy.
research Uncharted Waters: The Impact of NGS and Machine Learning on Forensic Science
New DNA analysis and machine learning are advancing forensic science, improving accuracy and expanding into non-human applications.
research Estimating Scalp Moisture In A Hat Using Wearable Sensors
A hat with sensors can measure scalp moisture well, helping with hair care.
research An Analysis of Alopecia Areata Classification Framework for Human Hair Loss Based on VGG-SVM Approach
The VGG-SVM method accurately identifies and classifies stages of Alopecia Areata and other hair loss conditions.
research Prediction of Therapeutic Outcomes of Female Pattern Hair Loss Patients Based on Clinical Features with Application of Artificial Intelligence
Artificial intelligence can accurately predict hair growth and treatment results in female pattern hair loss patients, with age of onset and duration being key factors.
research Enhancing Hairfall Prediction: A Comparative Analysis of Individual Algorithms and an Ensemble Method
Combining multiple algorithms predicts hair fall more accurately than using single algorithms.
research DNN-DTIs: Improved Drug-Target Interactions Prediction Using XGBoost Feature Selection and Deep Neural Network
The DNN-DTIs method accurately predicts drug-target interactions and is useful for drug repositioning.
research LabeledIn: Cataloging Labeled Indications for Human Drugs
Researchers created LabeledIn, a detailed list of drug uses, showing the importance of human input in making such lists.
research Role of Drug Repurposing in Sustainable Drug Discovery
Drug repurposing speeds up drug development, saves money, and has led to about a third of new drug approvals.
research Integrated Single-Cell Chromatin and Transcriptomic Analyses of Human Scalp Reveal Etiological Insights Into Genetic Risk for Hair and Skin Disease
The research provided new insights into the genetic factors contributing to hair loss and skin conditions by analyzing individual cells from the human scalp.
research The 3D Bioprinted Scaffolds for Wound Healing
3D bioprinting improves wound healing by precisely creating scaffolds with living cells and biomaterials, but faces challenges like resolution and speed.