Statistical Modeling and Analysis of Radio-Induced Adverse Effects Based on In Vitro Data

Polina ARSENTEVA
PhD Student, Université de Bourgogne
October 26th, 2023, 4 PM

Abstract
This talk addresses the problem of adverse effects induced by radiotherapy on healthy tissues. The goal is to propose a mathematical framework to compare the effects of different irradiation modalities, to be able to ultimately choose those treatments that produce the minimal amounts of adverse effects for potential use in the clinical setting. The adverse effects are studied through the in vitro omic response of human endothelial cells. We encounter the problem of extracting key information from complex temporal data that cannot be treated with the methods available in literature. We model the fold changes, the object that encodes the difference in the effect of two experimental conditions, in a way that allows to take into account the uncertainties of measurements as well as the correlations between the observed entities. We construct a distance, with a further generalization to a dissimilarity measure, allowing to compare the fold changes in terms of all the important statistical properties. Finally, we propose a computationally efficient algorithm performing clustering jointly with temporal alignment of the fold changes. The key features extracted through the latter are visualized using two types of network representations, for the purpose of facilitating biological interpretation.

Supplementary Materials
Website: https://parsenteva.github.io/
Code: https://github.com/parsenteva/scanofc