Machine learning enhances proteomics by optimizing peptide identification, structure prediction, and biomarker discovery.

A Hybrid Machine Learning Framework for Early Diabetes Prediction in Sierra Leone Using Feature Selection and Soft-Voting Ensemble ...

Objective This study aims to evaluate relationships between self-reported fine motor ability and quality of life (assessed by life satisfaction and life problems) from people with spinal cord injury ...

Abstract: Simulating multivariate random variables is essential in data analytics as it allows for more accurate modeling, improved decision-making, and a better understanding of complex systems and ...

Objective Cardiovascular diseases (CVD) remain the leading cause of mortality globally, necessitating early risk ...

Abstract: The past decade has witnessed the success of deep learning-based multivariate time-series forecasting in Internet of Things (IoT) systems. However, dynamic variable correlation remains a ...

Objective To determine whether liquefied petroleum gas (LPG) can reduce perinatal mortality in a setting with high reliance on biomass fuels for cooking.Design Community-based two-arm parallel cluster ...

Current models of mortality risk after heart failure (HF) rely primarily on cardiac-specific clinical variables and may underestimate risk in elderly East Asian patients. Researchers from Japan used ...

Learn how to distinguish marginal costs by exploring their relationship with fixed and variable costs in production.

Once installed, it can be called either by instantiating a family of random hash functions, or using the default instantiated functions: In 1983, G. N. N. Martin and Philippe Flajolet introduced the ...

BACKGROUND: Mental stress-induced myocardial ischemia is often clinically silent and associated with increased cardiovascular risk, particularly in women. Conventional ECG-based detection is limited, ...