Machine learning framework for T-cell receptor repertoire-based viral diagnostics

Current standards of viral diagnostics rely on in-vitro methods detecting either genome or proteins of a pathogen or host antibodies against pathogenic antigens. As a result, multiple assays are required when a sample is screened for several viruses, making the process time-demanding and cost-ineffective. Moreover, some of the methods fail in the case of acute and latent infections. With this FWO-SB project, I will investigate the potential of T cell receptor (TCR) repertoires to overcome this shortcoming and introduce a new approach for the simultaneous diagnosis of multiple viral infections. To discover the TCR signatures that differ between infected and uninfected individuals, I will search for pathogen-associated patterns in TCR repertoires by applying state-of-the-art immunoinformatics and machine learning methods. The obtained results will be used to build a classification model that utilizes the TCR repertoire to predict whether an individual is virus-positive or virus-negative. The insights from this project will broaden our understanding of pathogen-induced TCR repertoire changes and serve as a foundation for the development of a computational diagnostic framework. This will have a high impact on the broad field of diagnostics as the TCR repertoire is playing an important role in various non-infectious diseases, such as cancer and autoimmune diseases.