research A probabilistic Boolean model on hair follicle cell fate regulation by TGF-β
TGF-β and TNF influence hair follicle cell fate, with TNF being more effective in triggering cell death.
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150-180 / 1000+ results research A probabilistic Boolean model on hair follicle cell fate regulation by TGF-
research Foundations of Bayesian Networks
Bayesian networks are tools for modeling variables' probabilistic relationships, which can efficiently represent complex probabilities and help in making inferences.
research A Monte Carlo Simulation Framework to Quantify Platelet Dose Variability in Platelet-Rich Plasma Therapies
Platelet dose in therapies varies greatly due to factors like injected volume and concentration.
research Artificial neural networks algorithms for prediction of human hair loss related autoimmune disorder problem
Artificial neural networks can accurately diagnose Alopecia Areata.
research A mechanism-informed deep neural network enables prioritization of regulators that drive cell state transitions
A new neural network helps identify key regulators in cell changes, aiding in understanding diseases and finding new treatments.
research A comparative study of linear and nonlinear ANNs as classifiers of animal hair fibers
Nonlinear artificial neural networks are better at identifying different types of animal hair than linear ones.
research Hair Loss Diagnosis Using Artificial Neural Networks
Neural networks can effectively predict hair loss.
research Prediction of Alopecia Areata using CNN
A new CNN model can detect Alopecia Areata with 98% accuracy.
research The Dark Side of the Language: Syntax-Based Neural Networks Rivaling Transformers in Definitely Unseen Sentences
Syntax-based neural networks can match Transformers in handling unseen sentences.
research BaldPredict: AI-Driven Prediction Model for Alopecia Areata
AI can improve alopecia areata diagnosis with high accuracy.
research Predicting Hair Loss with AI: A Deep Learning Framework Combining Genetic and Scalp Health Data
AI can predict hair loss by analyzing genetic, scalp, and lifestyle data.
research Methods of Transfer Learning for Multiclass Hair Disease Categorization
Transfer learning with three neural network architectures accurately classifies hair diseases.
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 Ensemble of pre-learned deep learning model and an optimized LSTM for Alopecia Areata classification
The new model improves Alopecia Areata classification accuracy to 93.1%.
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 Revolutionizing Hair Fall Analysis: The Advanced Precipitation U-Net Model
The Advanced Precipitation U-Net Model improves early hair fall detection with 92% accuracy.
research Improved Hidden Markov Algorithm Based on Bayes in Low Dose CT Images
The improved algorithm enhances low-dose CT image quality significantly better than other methods.
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 AB-MTEDeep Classifier Trained with AAGAN for the Identification and Classification of Alopecia Areata
The AI model accurately classifies Alopecia Areata with 96.94% accuracy.
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 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 KRDQN: An Interpretable Prediction Framework for Adverse Drug Reactions via Knowledge–Graph Reinforced Deep Q-Learning
KRDQN effectively predicts adverse drug reactions with high accuracy and clear explanations.
research Leveraging deep neural networks to uncover unprecedented levels of precision in the diagnosis of hair and scalp disorders
A new AI model diagnoses hair and scalp disorders with 92% accuracy, better than previous models.
research Quantitative analysis and development of alopecia areata classification frameworks
A new algorithm effectively classifies Alopecia Areata, aiding early detection and treatment.
research Hair Disease Classification Using Convolutional Neural Network (CNN) Algorithm with VGG-16 Architecture
The system can accurately classify hair diseases with 94.5% accuracy using a CNN.
research Hair cluster detection model based on dermoscopic images
The new model detects hair clusters more accurately and efficiently, helping with early hair loss treatment and diagnosis.
research An Explainable AI-Based Decision Support System for Teaching and Classifying Hair Loss Types
The AI system accurately classifies hair loss types and explains its decisions.
research Neurospectrum: A Geometric and Topological Deep Learning Framework for Uncovering Spatiotemporal Signatures in Neural Activity
Neurospectrum effectively analyzes neural signals to predict and identify brain activity patterns better than traditional methods.
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 DETECTION OF HAIR FALL AND SCALP DISORDERS THROUGH ML AND IMAGE PROCESSING
AI can effectively detect hair and scalp disorders from images.