We analyzed 4,427 patients with MDS divided into training and validation cohorts. Deep learning methods were applied to integrate and impute clinical/genomic features. Clustering was performed by ...

Imagine this: you’ve just received a dataset for an urgent project. At first glance, it’s a mess—duplicate entries, missing values, inconsistent formats, and columns that don’t make sense. You know ...

How can artificial intelligence improve the analysis of chromatographic data? Artificial intelligence (AI) is a powerful and ...

Data quality management is important for enterprise data accuracy and integrity. These frameworks can help you identify and fix problems before they impact your business. While companies may share ...

To address critical research challenges in heavy-ion collisions at Fermi-energy regimes—specifically targeting the nuclear equation of state (nEoS), Femtoscopic interferometry of light nuclei, and ...

This article and associated images are based on a poster originally authored by Matthew Chung, William Guesdon, Kai Lawson-McDowall and Matthew Alderdice and presented at ELRIG Drug Discovery 2025 in ...

Data modeling refers to the architecture that allows data analysis to use data in decision-making processes. A combined approach is needed to maximize data insights. While the terms data analysis and ...

Using an expert consensus-based approach, a netball video analysis consensus (NVAC) group of researchers and practitioners was formed to develop a video analysis framework of descriptors and ...

This framework and aide-mémoire present a framework for integrating gender and identity analysis into the assessment of and response to hybrid threats. RUSI's project on Enhancing NATO Counter Hybrid ...