Research team
Machine learning framework for T-cell receptor repertoire-based viral diagnostics.
Abstract
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.Researcher(s)
- Promoter: Laukens Kris
- Co-promoter: Meysman Pieter
- Fellow: Postovskaya Anna
Research team(s)
Project type(s)
- Research Project
Transferable deep learning for sequence based prediction of molecular interactions.
Abstract
Machine learning can be used to elucidate the presence or absence of interactions. In particular for life science research, the prediction of molecular interactions that underlie the mechanics of cells, pathogens and the immune system is a problem of great relevance. Here we aim to establish a fundamentally new technology that can predict unknown interaction graphs with models trained on the vast amount of molecular interaction data that is nowadays available thanks to high-throughput experimental techniques. This will be accomplished using a machine learning workflow that can learn the patterns in molecular sequences that underlie interactions. We will tackle this problem in a generalizable way using the latest generation of neural networks approaches by establishing a generic encoding for molecular sequences that can be readily translated to various biological problems. This encoding will be fed into an advanced deep neural network to model general molecular interactions, which can then be fine-tuned to highly specific use cases. The features that underlie the successful network will then be translated into novel visualisations to allow interpretation by biologists. We will assess the performance of this framework using both computationally simulated and real-life experimental sequence and interaction data from a diverse range of relevant use cases.Researcher(s)
- Promoter: Laukens Kris
- Co-promoter: Bittremieux Wout
- Co-promoter: Meysman Pieter
- Fellow: Postovskaya Anna
Research team(s)
Project type(s)
- Research Project