Integrate data from multiple sources and owners with machine learning to classify and protect sensitive data.
An integrated web platform combining modelling with data sharing and reuse, revolutionising the way data is accessed to inform, accelerate and empower models.
Maximise integrates data from multiple sources and employs machine learning to classify data & protect sensitive data that is shared.
Case Study - Maximise
Funded by the NC3Rs, we partnered with Syngenta, Corteva and the University of York to develop the Maximise software platform, combining modelling with data sharing and reuse from multiple data owners and automating toxicity prediction for new formulations.
Maximise has been applied to predict Global Harmonised System (GHS) toxicity classification for novel formulations, transparently reducing the need for animal toxicology studies. Users create new formulations and their toxicity is automatically predicted based on their own and other users data (protecting the sensitive data that is shared). Reports are generated outlining the rationale behind the predicted toxicity ready to be shared with regulators.