Imagine your liver is like a filter that slowly gets clogged with fat. Before it becomes permanently damaged, there are tiny chemical warning signs in the blood. This work uses AI and biological models to find those specific signs so doctors can stop the damage before it's too late.
Liver disease (MASLD/NASH) is difficult to diagnose early and lacks effective treatments, creating a massive healthcare burden. Current models fail to reflect human disease complexity, leaving a gap in early prevention strategies.
A systems biology blueprint and machine learning models that identify specific biomarkers and molecular targets for each stage of liver disease progression.
If you are a drug discovery firm dealing with high failure rates in liver disease trials — this project developed a blueprint for molecular targets that allows for the creation of more effective, targeted drugs against MASH.
If you are a medical testing laboratory dealing with the difficulty of diagnosing early-stage liver inflammation — this project developed new biomarkers that improve existing detection methods for patients transitioning from health to disease.
If you are a health-tech AI startup dealing with a lack of high-quality disease progression data — this project developed in silico machine learning approaches using multimodal data from human cohorts and biobanks.
Based on available project data, specific pricing or cost structures for the biomarkers and tools are not provided.
The project uses extensive human cohorts and biobanks to ensure findings are validated with real-world data, suggesting a path toward scalable clinical application.
Based on available project data, the specific IP and licensing agreements are not detailed in the summary.
The project is active from 2022-12-01 to 2026-11-30, indicating that final results and validated targets will be available toward the end of 2026.
It provides healthcare professionals with tools and knowledge to establish new guidelines for the prevention and treatment of inflammation-driven disease.
The consortium is heavily research-oriented with 26 partners, including 11 research organizations and 9 universities. However, there is a significant commercial bridge with 4 industry partners (including 4 SMEs), representing a 15% industry ratio, which suggests the project is designed with a path toward commercial application in the hepatology and diagnostics markets.
Contact the Universidad Complutense de Madrid
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