Imagine your immune system is like a security team that sometimes attacks the wrong person after being tricked by a virus. Most of the time, we don't know which specific germ started the mistake for diseases like Multiple Sclerosis or Lupus. This work scans 600,000 possible triggers to find the exact 'criminal' and uses AI to predict who will get sick and how to treat them.
Clinicians cannot currently identify the specific infectious triggers for most autoimmune diseases, making it impossible to predict which patients will develop chronic conditions or how they will respond to treatment.
A multi-omics data analysis pipeline and ML algorithms to link 600,000 antigens to disease progression in 6,000 patients.
If you are a drug discovery firm dealing with unknown targets for autoimmune diseases — this project developed a pipeline to screen 600,000 antigens that identifies new biomarkers for drug development.
If you are a precision medicine lab dealing with inaccurate disease prognosis — this project developed ML algorithms that predict disease progression and treatment response for 6,000 patients.
If you are an immunology research company dealing with the complexity of the microbiota-immune axis — this project developed a multi-omics profiling method to disentangle genetic and environmental factors.
Based on available project data, the specific commercial pricing is not listed, though the EU provided a contribution of EUR 7,185,361 for the research phase.
Yes, the project describes the combination of assays and ML as a broadly applicable pipeline that can be used for studying the interplay of any other infectious diseases and immune-related NCDs.
Based on available project data, specific licensing terms are not provided, but the project involves 13 partners including one industry SME.
The project period runs from 2024-01-01 to 2028-12-31, indicating a multi-year development timeline.
The system integrates multi-omics data, HLA genotyping, and adaptive immune receptor genes from a cohort of 6,000 patients.
The consortium is heavily academic, consisting of 8 universities and 4 research institutes across 8 countries. With only 1 industry partner (an SME), the project has a low industry ratio of 8%, suggesting the current focus is on fundamental discovery and validation rather than immediate commercial productization.
Contact the Medical University of Vienna (Medizinische Universitaet Wien)
Contact us to identify potential licensing opportunities for the ML-based diagnostic pipeline.
