Machine learning algorithms that output human-readable equations and design rules are transforming how electrocatalysts for ...

Traditional computational electromagnetics (CEM) methods—such as MoM, FEM, or FDTD—offer high fidelity, but struggle to scale ...

A new software tool, ovrlpy, improves quality control in spatial transcriptomics, a key technology in biomedical research. Developed by the Berlin Institute of Health at Charité (BIH) in international ...

Deep learning is increasingly used in financial modeling, but its lack of transparency raises risks. Using the well-known Heston option pricing model as a benchmark, researchers show that global ...

Deep learning analysis of FDG PET-CT improves survival prediction in non-metastatic breast cancer, outperforming standard staging and single modality models while supporting interpretable and ...

Deep learning has become a powerful tool in quantitative finance, with applications ranging from option pricing to model calibration. However, despite its accuracy and speed, one major concern remains ...

Abstract: Although interpretable deep learning achieved significant progress in recent years, their data-driven nature often leads to interpretation results that are inconsistent with established ...

This important study describes a deep learning framework that analyzes single-cell RNA data to identify a tumor-agnostic gene signature associated with brain metastases. The identified signature ...

Introduction: Translating human genetic findings, such as genome-wide association studies (GWAS) to pathobiology and the discovery of therapeutic target remains a challenge for Atrial Fibrillation (AF ...

ABSTRACT: Understanding why a machine learning model makes a certain prediction is just as critical as how accurately it predicts, especially when it comes to diagnosing and treating cardiovascular ...