To support the biomarker and data analytics in PoCCardio, the project has welcomed The Cyprus Institute of Neurology and Genetics (CING), and specifically the CING Bioinformatics Department (C-BIG), as a new partner.

With the enrolment of 1,800 patients with prevalent myocardial infarction (MI) in the project’s clinical trial, a vast amount of clinical data, biomarker readings, and outcome data will be generated, making an integrated and holistic bioinformatics approach essential.

As experts in Computational Diagnostics and Therapeutics within Systems Bioinformatics and Medical Informatics, C-BIG will contribute to this requirement by applying a systemic approach that considers a disease within the framework of complex biological networks. Systems Bioinformatics harnesses the powerful methods of network science to both integrate information across different levels and sources, in the form of networks, as well as to extract information from the features of complex biological networks. This approach can be crucial in understanding the pathophysiology of a disease, improving diagnosis, and consequently providing personalised treatment.

“We are extremely delighted to be able to welcome CING to the project. The Bioinformatics Department at CING is the ideal bridge between the technical and clinical teams, combining clinical experience with technical know-how to drive Systems Bioinformatics-based support, which has the potential to help predict extremely high-risk patients and their treatment responses, leading to advanced diagnostic services for patients”, states Project Coordinator, Prof. Hans Peter Dimai from the Medical University of Graz.

Network generation & risk prediction
In PoCCardio, C-BIG will employ network generation tools for unravelling functionally and clinically meaningful interactions between biomarkers and create a network based on the genomic and proteomic biomarkers used in PoCCardio and existing public data of proteins and genetic polymorphisms relevant to cardiovascular diseases. From these networks, C-BIG will train algorithms to effectively predict the risk of an MI event.

Diagnostic protocol in PoCCardio PoC device
Based on the enriched biomarker readings, C-BIG will then develop a Systems Bioinformatics MI risk prediction protocol that will utilise the output from the PoCCardio PoC device. This will allow fast and effective analysis of biomarker readings by leveraging high-specificity, low-cost, and powerful artificial intelligence, thus supporting the PoCCardio objective of a timely and cost-efficient determination of the risk profile.

Hop-on partner
CING joins the project as a Hop-on Facility, which is an action in the Horizon Europe sub-programme ‘Widening participation and spreading excellence’, allowing research institutions from Widening countries to join already ongoing R&I actions.

Image: “Social Network Analysis Visualization” by Martin Grandjean, licensed by CC BY-SA 3.0