Unlocking biomedical data sharing: A structured approach with digital twins and artificial intelligence (AI) for open health sciences.

ABSTRACT:

Objective

Data sharing promotes the scientific progress. However, not all data can be shared freely due to privacy issues. This work is intended to foster FAIR sharing of sensitive data exemplary in the biomedical domain, via an integrated computational approach for utilizing and enriching individual datasets by scientists without coding experience.

Methods

We present an in silico pipeline for openly sharing controlled materials by generating synthetic data. Additionally, it addresses the issue of inexperience to computational methods in a non-IT-affine domain by making use of a cyberinfrastructure that runs and enables sharing of computational notebooks without the need of local software installation. The use of a digital twin based on cancer datasets serves as exemplary us

SUBMITTER: Jean-Quartier C 

PROVIDER: S-EPMC11394355 | biostudies-literature | 2024 Jan-Dec

REPOSITORIES: biostudies-literature