Machine learning methods, especially Transformer architectures, have been widely employed in single-cell omics studies. However, interpretability and accurate representation of out-of-distribution (OOD) cells remains challenging. Inspired by the glob...
OBJECTIVE: To develop and validate a machine learning framework combined with a nomogram for predicting recurrence after radical gastrectomy in patients with vascular and neural invasion.
OBJECTIVE: IgA nephropathy (IgAN) is the most common form of glomerulonephritis worldwide, characterized by immune complex deposition in the glomerular mesangium, leading to mesangial hypercellularity, persistent microhematuria, proteinuria, and prog...
BACKGROUND: Coronary angiography remains the gold standard for diagnosing coronary artery disease (CAD), but its invasive nature limits its applicability for widespread screening. Identifying non-invasive molecular markers could improve CAD classific...
BACKGROUND: As the global population ages, the economic burden of dementia continues to rise. Social isolation-which includes limited social interaction and feelings of loneliness-negatively affects cognitive function and is a significant risk factor...
Starch is a fundamental carbohydrate with nutritional and physicochemical properties governed by relative proportions of amylose and amylopectin. Variations in amylose-to-amylopectin ratio significantly influence starch digestibility, texture, glycem...
Epileptic seizures can occur unpredictably, making real-time monitoring and early warning systems critical, especially in neonatal patients, where timely intervention can significantly improve outcomes. Neonatal seizures are often subtle and difficul...
Schizophrenia (SZ) and Bipolar disorder (BD) share genetic and cerebral abnormalities, supporting an expanded continuum hypothesis. In this paper, we aim to better characterize differences and commonalities of gray and white matter features between S...
Neural networks : the official journal of the International Neural Network Society
Jun 21, 2025
Domain generalization is proposed as an approach capable of solving the domain shift challenge, which aims at generalizing knowledge learned from multiple source domains with different distributions to the target domain that is invisible during the t...
In this study, multivariate analysis (MVA) and machine learning (ML), combined with UHPLC-HRMS, were used to evaluate the freshness of apples for juice production based on the analysis of freshly squeezed apple juice. A total of eight stages of apple...
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.