Sadnan Al Manir

The paper introduces FAIRSCAPE, a framework for packaging biomedical datasets with metadata to enhance AI-readiness, explainability, and FAIR compliance.

The paper defines AI-readiness criteria for biomedical data, encompassing seven dimensions, and evaluates them across flagship datasets to ensure ethical and explainable AI practices.

The paper introduces the CM4AI project, which aims to create AI-ready datasets of human cell architecture using multimodal data from cell lines.

The paper uses time-series analysis on NICU vital signs to identify features predicting all-cause mortality within 7 days, finding five algorithms that outperform others with an AUC of 0.83.

FAIRSCAPE provides a framework for creating FAIR and reproducible biomedical analytics by generating machine-interpretable provenance metadata for computational results.