Grégoire Thomas - squ4re: Development of clinical diagnostic models – Mitigating the risks
Nicolas De Neuter - UAntwerpen: Classification models to predict and explain CMV status
About the Lunch Talks
The Biomina Lunch Talks are an initiative of a number of young researchers in the biomina network and is sponsored by the Flemish Government. We aim to stimulate the interaction between researchers from different disciplines who encounter bioinformatics and computational biology, and consequently we focus on a broad and multidisciplinary public. With this informal medium we would like to provide a platform where knowledge and experience can be presented and exchanged, across partners from both academia and industry. In this manner we have had the pleasure to welcome speakers from various institutes such as the University of Antwerp, the Institute of Tropical Medicine, Janssen Pharmaceutica, the Antwerp University Hospital and Open Analytics. Last, but not least, these sessions can provide a great opportunity for young researchers to acquaint themselves with new ideas and methods in the field of bioinformatics and medical informatics.
Grégoire Thomas, squ4re
Title: Development of clinical diagnostic models – Mitigating the risks
Nicolas De Neuter, UAntwerpen
Title: Classification models to predict and explain CMV status
Abstract: Cytomegalovirus (CMV) is one of several herpes viruses that remain present in the human body for life after initial infection. The CMV virus presents several health risks. CMV reactivation is a frequent problem in immunocompromised individuals and CMV seropositivity is a risk factor in for example transplants. In this presentation, we will cover two CMV related problems and show how we tackled these problems with the use of machine learning models. First, we show that by using classification models, we can predict the CMV serostatus from a signature CD4+ memory T cell receptor pattern. Second, we explore risk factors for CMV reactivation in kidney transplant patients and show the use of a classification model to predict CMV reactivation.