Québec City – September 19, 2026 -- Linearis Labs has secured funding for two projects under the Terry Fox Research Institute's (TFRI) Digital Health Innovation Fund, part of a combined $40.9 million investment spread across 14 pan-Canadian project teams and 55 partners.
TFRI backs 14 projects with $40.9 million to advance AI-driven precision medicine
The funding, distributed through TFRI's Digital Health & Discovery Platform (DHDP), targets AI-driven health data sharing for precision medicine nationwide. TFRI President and Scientific Director Dr. Jim Woodgett said the initiative reflects research and enterprise coming together to drive innovation.
Linearis Labs pairs with five partners on cancer treatment prediction project
The first funded project, Multi-Omics Data to AI Disease Signature Discovery, unites Linearis Labs with MRM Proteomics, Simmunome, the Jewish General Hospital/CIUSSS du Centre-Ouest-de-l'Île-de-Montréal, and Nova Scotia Health. The team will apply AI to integrated genomic, proteomic, and metabolomic data to predict which treatments are most effective for patients with lung, breast, or colorectal cancers.
Second project targets biomarkers for neurodegenerative disease monitoring
The second initiative, AI-Enabled Biomarker Discovery and Therapeutic Monitoring in Neurodegenerative Disease, pairs Linearis Labs with Nanil Therapeutics. The collaboration will use secure, standardized health data and AI to identify biological signals for studying and monitoring neurodegenerative conditions.
Alexandre Le Bouthillier, CEO of Linearis Labs, said combining genomics, proteomics, and TMIC's 1,780 metabolomics GigaKit panel with the company's AI reasoning layer allows researchers to see the whole picture of a disease rather than a fragment, enabling faster diagnosis and treatment matched to individual patients.
Federated learning infrastructure lets institutions analyze data without moving it
Both projects will run on DHDP's federated learning infrastructure, which allows researchers across institutions to analyze shared data without physically transferring it. The approach preserves patient privacy while enabling cross-institutional AI discovery and opening new investment channels in the sector.