In earlier projects, Biomax developed disease knowledge bases containing integrated clinical data with image analysis pipelines and computational models to generate clinical decision support systems. These knowledge bases are already used in clinics to support decisions in diagnosis, prognosis and even prevention. Building on this experience, the METSY project enables Biomax to further tap its expertise to translate a Systems Medicine approach into clinical practice for mental health.
The METSY project aims to identify, prioritize and evaluate multi-modal blood and neuroimaging markers for prediction and monitoring of psychotic disorders and associated metabolic co-morbidities.
The METSY project will generate brain imaging data using magnetic resonance imaging (MRI) and position emission tomography (PET), metabolite profiling and clinical data from about 500 patients recruited at four sites in Spain, the United Kingdom and Finland.
This four-year project involves seven partners from industry, research and clinics who will search for associations between lipid metabolism, psychotic disorders and metabolism-related diseases such as obesity and diabetes.
Using the BioXM™ Knowledge Management System, Biomax will combine the data acquired in the METSY project with existing knowledge sources about brain structure and function, and extend them with information about psychosis and genetic factors derived from the integrated literature mining module.
The result will be a semantic network of psychosis-specific knowledge generated from associations between structures, functions and molecules (genes, proteins and metabolites). This can be overlaid with patient-specific data and, as such, becomes suitable for algorithmic analysis by the METSY partners. The partners use network inference to identify and evaluate multi-modal blood and neuroimaging biomarkers that could be used to predict and monitor psychotic disorders like schizophrenia in the context of metabolic diseases.
If specific disease-associated patterns are detected in the project, the network can be searched for potential explanations. After validation, the knowledge base can be used to build a clinical decision support system to bring the results directly into clinical practice.