Support vector machines improve classification by mapping inseparable signals into higher-dimensional spaces. Random forest models, through ensemble decision trees, increase robustness against ...

The other is to invest in high-fidelity digital twins which are deemed to be powerful but costly, data-hungry, and often slow to implement. A practical middle ground is emerging: the disruption-aware ...

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

A deceptively simple question underlies many global environmental policies: where, exactly, are the world’s forests? A new study suggests the answer depends heavily on which map one consults—and that ...

Objective Cardiovascular diseases (CVD) remain the leading cause of mortality globally, necessitating early risk identification to improve prevention and management strategies. Traditional risk ...

Background Patients with heart failure (HF) frequently suffer from undetected declines in cardiorespiratory fitness (CRF), which significantly increases their risk of poor outcomes. However, current ...

Researchers from the University of British Columbia argue that a widely used method to understand and predict flood risk has led scientists to miscalculate how forests can prevent major flooding. The ...

In a collaboration between several labs at UNC Lineberger, researchers have defined cancer-associated fibroblast (CAF) subtypes that are clinically robust, prognostic, and predictive of immunotherapy ...

Background Gut microbiota dysbiosis is linked to autism spectrum disorder (ASD) in children. However, the role of bacterial ...

BACKGROUND: Venous thromboembolism (VTE) is a leading cardiovascular disease, yet its etiology is incompletely understood. This study used large-scale, high-throughput aptamer-based proteomics to ...