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.
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research Bayesian Hidden Markov Modelling of Blood Type Distribution for Covid-19 Cases Using Poisson Distribution
Blood type affects COVID-19 infection rates differently in Europe and Africa.
research A Hierarchical Bayesian Hidden Markov Model for Multi-Dimensional Discrete Data
Non-denatured soybean extracts are effective for skin care, offering skin lightening, anti-aging, and UV protection benefits.
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 Balancing metabolic optimization and reproductive safety in Polycystic Ovary Syndrome: a Bayesian-informed framework for GLP-1 receptor agonists
GLP-1 receptor agonists can help manage PCOS metabolism but need careful use before conception.
research Evaluating statistics in clinical trials: Making the unintelligible intelligible
Doctors need to understand statistics to properly evaluate clinical trials for patient care.
research AI06 ALUDWIG: an automated artificial intelligence-based assessment of female androgenic alopecia
ALUDWIG can help standardize female hair loss assessment from a single image.
research The Effects of the Chemical Relaxer Lye on Hair Cortisol Concentrations
Lye relaxers don't significantly change hair cortisol levels.
research Select the most correct answers – multiple answers possible for questions 1–11
Medical practitioners need to understand basic statistics to properly evaluate clinical trials and avoid unethical designs.
research Effective holistic characterization of small molecule effects using heterogeneous biological networks
Integrating biological networks improves drug repurposing and ADR prediction.
research Relative efficacy of monotherapies for male pattern hair loss: protocol for a network meta-analysis study
research DrugNet: Network-based drug–disease prioritization by integrating heterogeneous data
DrugNet effectively identifies new uses for existing drugs and may save resources in drug development.
research The Relative Efficacy and Safety of Therapies for Alopecia Areata: Protocol for a Network Meta-Analysis Study
research The comparative effect of monotherapies for male androgenetic alopecia: a protocol for network meta-analysis study
The document aims to compare the effectiveness of different single treatments for male pattern hair loss.
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 Group-ICA Model Order Highlights Patterns of Functional Brain Connectivity
Choosing the right model order in brain connectivity analysis can affect the detection of differences between healthy individuals and those with seasonal affective disorder.
research Peer Review #3 of "Computational drug repositioning based on side-effects mined from social media (v0.2)"
The study improved and was accepted despite initial concerns about data clarity, methodology, and potential overfitting.
research Relative efficacy of minoxidil and 5-alpha-reductase inhibitors in the treatment of male androgenetic alopecia: protocol for a network meta-analysis study
research Towards Fairer Health Recommendations: finding informative unbiased samples via Word Sense Disambiguation
Fine-tuned BERT models are better than LLMs for detecting bias in medical data.
research PROMETHEUS: Automating Deep Causal Research Integrating Text, Data and Models
PROMETHEUS helps organize and evaluate causal claims from large language models.
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 A weighted non-negative matrix factorization approach to predict potential associations between drug and disease
WNMFDDA effectively predicts drug-disease associations.
research Peer Review #3 of "Computational drug repositioning based on side-effects mined from social media (v0.3)"
Reviewers criticized the study's methods and suggested focusing on drug mechanisms instead of repositioning due to social media data quality concerns.
research Peer Review #2 of "Computational drug repositioning based on side-effects mined from social media (v0.1)"
Reviewers suggested the study on finding new drug uses through social media side-effects needs better methods and clearer limitations.
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 Addressing continuous data for participants excluded from trial analysis: a guide for systematic reviewers
The conclusion is that the risk of losing significance in meta-analysis results increases with smaller effects and more missing data, and using the median standard deviation for imputation is recommended.
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 Case-Only Trees and Random Forests for Exploring Genotype-Specific Treatment Effects in Randomized Clinical Trials with Dichotomous End Points
Case-only trees and random forests improve predictions of treatment effects in clinical trials.
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 Peer Review #2 of "Computational drug repositioning based on side-effects mined from social media (v0.2)"
The peer review highlighted the need for clearer data handling, questioned the study's validity, and recognized improvements from the original version.