HRT

Predicting Trajectories of Long COVID in Adult Women: The Critical Role of Causal Disentanglement

Early prediction of Post-Acute Sequelae of SARS-CoV-2 severity is a critical challenge for women's health, particularly given the diagnostic overlap between PASC and common hormonal transitions such as menopause. Identifying and accounting for these confounding factors is essential for accurate long-term trajectory prediction. We conducted a retrospective study of 1,155 women (mean age 61) from th...

A DNA foundation model predicts osteoporosis risk genes without proximity bias

Targets supported by human genetic associations are more than twice as likely to progress from clinical development to approval. Genome-wide association studies are the largest source of genetic evidence for disease risk but linking non-coding variants to effector genes remains a significant barrier to identifying causal targets. Current gene-mapping approaches suffer from proximity bias, largely ...

Transformer-Based Multi-Region Segmentation and Radiomic Analysis of HR-pQCT Imaging for Osteoporosis Classification

Osteoporosis is a skeletal disease typically diagnosed using dual-energy X-ray absorptiometry (DXA), which quantifies areal bone mineral density but o...

Transformer-Based Multi-Region Segmentation and Radiomic Analysis of HR-pQCT Imaging

Osteoporosis is a skeletal disease typically diagnosed using dual-energy X-ray absorptiometry (DXA), which quantifies areal bone mineral density but o...

Bridging the gap between Performance and Interpretability: An Explainable Disentangled Multimodal Framework for Cancer Survival Prediction

While multimodal survival prediction models are increasingly more accurate, their complexity often reduces interpretability, limiting insight into how...

Multimodal Survival Modeling and Fairness-Aware Clinical Machine Learning for 5-Year Breast Cancer Risk Prediction

Clinical risk prediction models often underperform in real-world settings due to poor calibration, limited transportability, and subgroup disparities....

Gender-Specific Osteoporosis Risk Prediction Using Longitudinal Clinical Data and Machine Learning

Osteoporosis is a silent yet debilitating disease that often remains undetected until fractures occur. While early prediction is crucial, most studies...

Comp2Comp: Open-Source Software with FDA-Cleared Artificial Intelligence Algorithms for Computed Tomography Image Analysis

Artificial intelligence allows automatic extraction of imaging biomarkers from already-acquired radiologic images. This paradigm of opportunistic imag...

Brainstem neurons coordinate the bladder and urethral sphincter for urination

Urination, a vital and conserved process of emptying urine from the urinary bladder in mammals, requires precise coordination between the bladder and ...

Retrospective multi-cohort validation of a real-world transcriptomics-guided machine learning model for treatment response prediction in breast cancer

Selection of systemic therapy for breast cancer remains largely empirical, particularly for chemotherapy, due to the lack of robust biomarkers that pr...

Evaluating the added value of salivary hormones in the context of menstrual cycle staging: A machine learning approach and app-implementation.

OBJECTIVE: Salivary hormone assessment is commonly used in menstrual cycle studies, but its validity for accurate menstrual cycle staging has been que...

Nutritionally Responsive PMv DAT Neurons Are Dynamically Regulated During Pubertal Transition.

Pubertal development is tightly regulated by energy balance. The crosstalk between metabolism and reproduction is orchestrated by complex neural netwo...

A Machine Learning Approach to Molecular Initiating Event Prediction Using High-Throughput Transcriptomic Chemical Screening Data.

Improved scalability of high-throughput RNA-sequencing technologies has contributed to their proposed use in regulatory contexts for chemical hazard i...

Development and validation of explainable machine learning models for female hip osteoporosis using electronic health records.

BACKGROUND: Hip fractures are associated with reduced mobility, and higher morbidity, mortality, and healthcare costs. Approximately 90% of hip fractu...

Opportunistic Osteoporosis Diagnosis via Texture-Preserving Self-Supervision, Mixture of Experts and Multi-Task Integration

Osteoporosis, characterized by reduced bone mineral density (BMD) and compromised bone microstructure, increases fracture risk in aging populations....

A flexible machine learning Mendelian randomization estimator applied to predict the safety and efficacy of sclerostin inhibition.

Mendelian randomization (MR) enables the estimation of causal effects while controlling for unmeasured confounding factors. However, traditional MR's ...

Integrative network and computational toxicology reveal the molecular mechanisms in PFOA-induced spermatogenic disorder.

Perfluorooctanoic acid (PFOA), a widely used industrial chemical, poses significant environmental and biological toxicity, particularly affecting repr...

Enhancing osteoporosis risk prediction using machine learning: A holistic approach integrating biomarkers and clinical data.

Osteoporosis (OP) affects approximately 18 % of the global population, with osteoporosis-associated fractures impacting up to 37 million people annual...

Application of machine learning algorithms in osteoporosis analysis based on cardiovascular health assessed by life's essential 8: a cross-sectional study.

BACKGROUND: Life's Essential 8 (LE8) for assessing cardiovascular health (CVH) has been demonstrated to be inversely associated with osteoporosis (OP)...

Comparative Analysis of Feature Extraction Methods and Machine Learning Models for Predicting Osteoporosis Prevalence.

This study systematically examined the impact of three feature selection techniques (Boruta, Extreme gradient boosting (XGBoost), and Lasso) for optim...