Accurate pharmaceutical demand forecasting is essential to ensure timely drug availability, reduce inventory costs, and improve operational efficiency in healthcare supply chains. However, existing ...

In silico methods for predicting the effects of multi-gene perturbations hold great promise for advancing functional genomics, computational drug discovery, and disease modeling. However, the ...

While retrieval-augmented generation is effective for simpler queries, advanced reasoning questions require deeper connections between information that exist across documents. They require a knowledge ...

For predicting relapse in 1,387 patients with early-stage (I-II) NSCLC from the Spanish Lung Cancer Group data (average age 65.7 years, female 24.8%, male 75.2%), we train tabular and graph machine ...

Image courtesy by QUE.com As we navigate the landscape of 2026, we find ourselves no longer merely using Machine Learning (ML) but ...

Graph machine learning (or graph model), represented by graph neural networks, employs machine learning (especially deep learning) to graph data and is an important research direction in the ...