Explore the use of Large Language Models for biological research, and define the best practices for doing so, using target discovery and validation as the initial use case
This project aims to convert unstructured assay protocol descriptions into a high-quality FAIR data set, and create standards for this information.
Our project aims to develop a pharmaceutical (CMC) process ontology, based on the ISA88/95 framework. This ontology will serve to standardize laboratory and plant production process recipes, and in turn, establish standardized definitions.
The goal of our project is to build an IDMP Ontology that enables deep, semantic interoperability based on FAIR principles to enhance and augment the existing IDMP standards.
The goal of the Social Media to Produce Real-World Evidence (RWE) project is to develop and publish key regulatory guidelines associated with the use of social media as real-world data (RWD) to produce RWE.
Project Charter: Ontologies proliferate. Their diversity limits semantic interoperability, increases costs, creates semantic siloes and slows the path to data centricity.
This project supported the implementation of the Findable, Accessible, Interoperable and Re-usable (FAIR) guiding principles in the life sciences by promoting best practices as well as supporting enabling methods and tools across industries.
This project delivered a semantic framework to standardize clinical trial operations and drive transformation for better patient outcomes
The Clinical Trial Environmental Impact project wishes to develop a dynamic model that compares the carbon footprint of a clinical trial in traditional, hybrid and decentralised settings.