FAIRSCAPE: An Evolving AI-readiness Framework for Biomedical Research.

Motivation: Artificial intelligence (AI) applications require explainability (XAI) for FAIR, ethical deployment, whether in the clinic or in the laboratory. Richly descriptive XAI metadata representing how pre-model data were obtained, characterized, transformed, and distributed, should be available along with the data prior to training and application of AI models.

Results: The FAIRSCAPE framework generates, packages, and integrates critical pre-model XAI descriptive metadata, including deep provenance graphs and data dictionaries with feature validation on uploaded data, software, and computations, with special reference to biomedical datasets.

Cell Maps for Artificial Intelligence: AI-Ready Maps of Human Cell Architecture from Disease-Relevant Cell Lines.

This article describes the Cell Maps for Artificial Intelligence (CM4AI) project and its goals, methods, standards, current datasets, software tools , status, and future directions. CM4AI is the in the U.S.

Discovery of signatures of fatal neonatal illness in vital signs using highly comparative time-series analysis.

To seek new signatures of illness in heart rate and oxygen saturation vital signs from Neonatal Intensive Care Unit (NICU) patients, we implemented highly comparative time-series analysis to discover features of all-cause mortality in the next 7 days. We collected 0.5 Hz heart rate and oxygen saturation vital signs of infants in the University of Virginia NICU from 2009 to 2019.

FAIRSCAPE: a Framework for FAIR and Reproducible Biomedical Analytics.

Results of computational analyses require transparent disclosure of their supporting resources, while the analyses themselves often can be very large scale and involve multiple processing steps separated in time. Evidence for the correctness of any analysis should include not only a textual description, but also a formal record of the computations which produced the result, including accessible data and software with runtime parameters, environment, and personnel involved. This article describes FAIRSCAPE, a reusable computational framework, enabling simplified access to modern scalable cloud-based components.

Design and characterization of a low profile NaI(Tl) gamma camera for dedicated molecular breast tomosynthesis.

A new low profile gamma camera is being developed for use in a dual modality (x-ray transmission and gamma-ray emission) tomosynthesis system. Compared to the system's current gamma camera, the new camera has a larger field of view (~20×25 cm) to better match the system's x-ray detector (~23×29 cm), and is thinner (7.3 cm instead of 10.