Lunch Talk 31st of January 2020

Date
Location

G.005, Middelheim Campus

Event type
Lunch Talk

Jurgen Claesen - UHasselt: Rule-based machine learning and expert systems in mass spectrometry

Elise Kuylen - UAntwerpen: Using individual-based models to model heterogeneity in susceptibility to measles

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.

Speakers

Jurgen Claesen, UHasselt

Title: Rule-based machine learning and expert systems in mass spectrometry

Abstract: Automated interpretation of spectra of organic matter is one of the oldest computational challenges in mass spectrometry. Since the early 1960s many approaches have been proposed to solve this problem. Among them, several rule-based systems and expert systems. In this presentation, I will illustrate how the translation of recorded masses to molecular formulas and amino-acid sequences benefitted and benefits from expert knowledge and rules.

Elise Kuylen, UAntwerpen:

Title: Using individual-based models to model heterogeneity in susceptibility to measles

Abstract: Over the last decade, individual-based models have become a popular tool for infectious disease modelling. These models represent each individual as a unique entity and thus allow us to model heterogeneity in the population at different levels. Modelling heterogeneity is particularly important for emerging diseases or diseases for which a high population immunity already exists.

Measles is one of these latter diseases. Today, many regions have reached the 95% vaccine coverage threshold proposed by the WHO to ensure herd immunity. However, we observe that measles outbreaks still occur in these regions. The threshold is based on models assuming homogeneity in social mixing and vaccination behaviour. However, susceptible individuals may be clustered in a number of ways, including by geographic location, by age, in schools, or in households. Using an individual-based model, Stride, we examined the impact of different levels of household-based susceptibility clustering on the occurrence and persistence of measles outbreaks in Flanders, Belgium. We found that higher levels of within-household clustering of susceptible individuals increase the risk, size and persistence of measles outbreaks. As such, ignoring within-household clustering of susceptibility as a source of heterogeneity in the population leads to underestimations of the efforts needed to mitigate and eliminate measles