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

To prevent algorithmic bias, the authors call for multivariable modeling frameworks that jointly incorporate biological sex, genetic ancestry, and gender-related life-course exposures.

Fragments of DNA from long-extinct human relatives still circulate in modern genomes, and in some cases they do more than ...

Machine learning is helping neuroscientists organize vast quantities of cells’ genetic data in the latest neurobiological cartography effort.

Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep ...

Abstract: Efficient task scheduling in cloud computing is critical to maintain the Quality-of-Service (QOS) while optimizing task allocation and reducing energy consumption. This paper proposes a ...

The workflow encompasses patient datacollection and screening, univariate regression analysis for initial variable selection, systematic comparison of 91 machine learning models,selection and ...

A machine learning algorithm used gene expression profiles of patients with gout to predict flares. The PyTorch neural network performed best, with an area under the curve of 65%. The PyTorch model ...

Abstract: This paper introduces a hybrid optimisation framework that integrates Genetic Algorithms (GAs) and Reinforcement Learning (RL) for the construction of high-order Runge–Kutta (RK) schemes.

ABSTRACT: As cloud computing continues to grow, efficient resource management remains a critical aspect of maintaining high-performance and cost-effective cloud infrastructures. CPU resource ...