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A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

Published on: August 16, 2017

The FAIRSCAPE AI-readiness Framework for Biomedical Research.

Sadnan Al Manir¹, Maxwell Adam Levinson¹, Justin Niestroy¹

¹University of Virginia School of Medicine.

Biorxiv : the Preprint Server for Biology

|January 7, 2025

Summary

This summary is machine-generated.

The FAIRSCAPE framework generates explainable AI (XAI) metadata for FAIR data, ensuring ethical AI deployment in research. This framework supports data provenance and characterization for robust AI model development.

Keywords:

AI-readiness Artificial Intelligence Data Provenance FAIR Principles Metadata Pre-Model AI Explainability

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Area of Science:

Biomedical Informatics
Artificial Intelligence
Data Science

Background:

Artificial intelligence (AI) applications necessitate explainability (XAI) for ethical and FAIR (Findable, Accessible, Interoperable, Reusable) deployment in clinical and laboratory settings.
Comprehensive XAI metadata detailing data acquisition, characterization, transformation, and distribution is crucial before AI model training and application.

Purpose of the Study:

To introduce the FAIRSCAPE framework for generating, packaging, and integrating essential pre-model XAI descriptive metadata.
To enhance the FAIRness and ethical considerations of biomedical datasets used in AI.

Main Methods:

The FAIRSCAPE framework generates deep provenance graphs and data dictionaries with feature validation for uploaded data, software, and computations.
It provides ethical and semantic dataset characterization, including licensing and availability information.
The framework integrates with NIH-recommended generalist repositories and is cloud-compliant, implemented in Python 3 with both server and client software, a REST API, and a JavaScript GUI.

Main Results:

FAIRSCAPE successfully generates and integrates critical pre-model XAI metadata, including provenance and data dictionaries.
The framework ensures ethical and semantic characterization of datasets, enhancing their FAIRness.
It offers flexible access via command-line, Python functions, a REST API, and a GUI.

Conclusions:

FAIRSCAPE provides a robust solution for generating and managing XAI metadata, crucial for FAIR and ethical AI deployment in biomedical research.
The framework's comprehensive features and flexible implementation facilitate seamless integration with existing data infrastructure and repositories.