Redwood AI Corp. (CSE: AIRX) (OTCQB: RDWCF) (Frankfurt: Y0N, WKN: A422EZ), a developer of an AI-powered platform for real-world applications across multiple critical industries, has been named a co-applicant in a competitive grant awarded by the Terry Fox Research Institute. The grant supports a research collaboration led by Onco-Innovations Limited (CBOE CA: ONCO) (OTCQB: ONNVF) (Frankfurt: W1H, WKN: A3EKSZ) to advance its SynoGraph(TM) oncology platform, according to a September 22 announcement. The project, titled “Causal AI to Predict First-in-Human Clinical Outcomes from Preclinical and Real-World Clinico-Genomic Data,” will investigate whether causal AI can improve predictions of first-in-human clinical outcomes using preclinical and real-world clinico-genomic data.
Redwood AI will contribute proprietary data integration and physicochemical modeling methods to expand SynoGraph’s predictive capabilities. The grant supports a project with an estimated budget of $3.5 million and reimburses eligible costs up to 25% of that amount. Academic partners, including the Michael Smith Genome Sciences Centre and the Centre de recherche du CHU de Québec-Université Laval, will provide clinico-genomic and imaging data for research using federated learning, which is intended to support analysis while protecting data privacy. This collaboration underscores the growing role of AI in drug development, where accurate prediction of clinical outcomes can reduce costs and accelerate timelines.
For investors, the involvement of Redwood AI Corp. in a Terry Fox-funded project signals external validation of its technology and potential expansion into the life sciences sector. The company’s shares trade on multiple exchanges, and its latest news and updates are available in its newsroom at https://ibn.fm/RDWCF. The collaboration also positions Onco-Innovations to enhance its SynoGraph platform, which could lead to more efficient oncology trials and better patient outcomes.
The project’s use of federated learning addresses privacy concerns by allowing analysis across distributed datasets without sharing raw data. This approach is increasingly important in healthcare research, where data sensitivity is paramount. By combining Redwood AI’s expertise in data integration and chemical modeling with academic partners’ clinico-genomic and imaging data, the project aims to create a robust predictive tool. If successful, SynoGraph could become a valuable asset in the oncology research toolkit, potentially attracting further investment and partnerships.
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