research Genetic prediction of male pattern baldness based on large independent datasets
New models predict male pattern baldness better than old ones but still need improvement.
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30-60 / 1000+ results research Exploring machine learning strategies for single-cell transcriptomic analysis in wound healing
Machine learning and single-cell analysis improve understanding and treatment of wound healing.
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 Minimizing Factual Inconsistency and Hallucination in Large Language Models
A new method improves the accuracy and reliability of language models by up to 42%.
research Bayesian measurement-error-driven hidden Markov regression model for calibrating the effect of covariates on multistate outcomes: Application to androgenetic alopecia
Model improves accuracy in predicting hair loss effects.
research Aspect-Level Drug Reviews Sentiment Analysis Based on Double BiGRU and Knowledge Transfer
A model called PM-DBiGRU was developed for analyzing sentiments in drug reviews, and it performed better than other models, but struggled with complex sentences and situations requiring background knowledge.
research Trichoscopy of Alopecia Areata: Hair Loss Feature Extraction and Computation Using Grid Line Selection and Eigenvalue
Researchers developed an algorithm for self-diagnosing scalp conditions with high accuracy using smart device-attached microscopes.
research Quantitative analysis and development of alopecia areata classification frameworks
A new algorithm effectively classifies Alopecia Areata, aiding early detection and treatment.
research Fail-tests of DNA methylation clocks, and development of a noise barometer for measuring epigenetic pressure of aging and disease
Elastic Net DNA methylation clocks are inaccurate for predicting age and health status; a "noise barometer" may better indicate aging and disease.
research Early prediction of alopecia areata using machine learning modeling of neuro stress immune signatures from multi datasets
A machine learning model can predict alopecia areata early using specific gene markers.
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 Altered polyamine profiling in the hair of patients with androgenic alopecia and alopecia areata
The conclusion is that balancing cost and carbon emissions in hybrid power systems is crucial, especially when high reliability is needed, but the model needs to consider all device efficiencies and distribution losses.
research Data-driven analysis to understand long COVID using electronic health records from the RECOVER initiative
Long COVID patients have more health issues than non-infected people.
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 human prenatal skin cell atlas reveals immune cell regulation of skin morphogenesis
Immune cells are essential for early hair and skin development and healing.
research Optimization of ultrasound-assisted extraction of bioactive compounds from Carthamus caeruleus L. rhizome: Integrating central composite design, Gaussian process regression, and multi-objective Grey Wolf optimization approaches
research Poster Abstracts
The model and estimator can predict drug exposure in kidney transplant patients well.
research Optimization of finasteride nano-emulsion preparation by using chemometrics approaches (and/or Box-Behnken design and regresion model)
research DEEP REINFORCEMENT LEARNING FOR SCALABLE CONTROL OF BOOLEAN MODELS IN THE CONTEXT OF CELLULAR REPROGRAMMING
pbn-STAC effectively finds strategies for cellular reprogramming using deep reinforcement learning.
research Closing the AI generalization gap by adjusting for dermatology condition distribution differences across clinical settings
Adjusting AI training data for skin condition distribution improves accuracy across different clinical settings.
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 Performance Evaluation of the Generative Pre-trained Transformer (GPT-4) on the Family Medicine In-Training Examination
GPT-4 performs well on medical exams but still needs human doctors for critical thinking.
research Discussion of “A hybrid phase I‐II/III clinical trial design allowing dose re‐optimization in phase III” by Andrew G. Chapple and Peter F. Thall
The new clinical trial design is promising but needs real-world trials to test its effectiveness and possible enhancements.
research MEGA PROTAC, MEGA DOCK-based PROTAC mediated ternary complex formation pipeline with sequential filtering and rank aggregation
MEGA PROTAC improves prediction and ranking of protein complexes better than existing methods.
research A flexible fixed-sequence testing method for hierarchically ordered correlated multiple endpoints in clinical trials
The flexible fixed-sequence testing method allows for more effective evaluation of multiple goals in a clinical trial while controlling the risk of false positives.
research Weakly supervised learning of biomedical information extraction from curated data
The method can effectively extract biomedical information without needing expert annotation, performing better than previous models.
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 A regulatory perspective on pharmacokinetic/pharmacodynamic modelling
The document concludes that PK/PD modeling is important for determining the safe and effective dosages of drugs.
research Xgboost Algoritmasıyla Polikistik Over Sendromu Teşhisi
XGBoost can effectively diagnose PCOS with 87% accuracy.