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Towards Assessing The Likelihood of Mutations in BRCA1/2 Genes with Interval and Dempster-Shafer Theory Based Methods
Germline mutations in BRCA1/2 genes are considered to lead to an increased risk of hereditary breast and ovarian cancer syndrome (HBOC). Modern genetic tests reliably identify BRCA1/2 mutations but are not necessarily helpful for everyone. Therefore, a preliminary step for arriving at specific suggestions concerning individual HBOC prevention and risk mitigation is the use of risk assessment tools that compute the likelihood of a mutation, for example, the Penn II risk model (https://pennmodel2.pmacs.upenn.edu/penn2/) and many others, including easy-to-use, questionnaire-type approaches. Because there are no true standards for data acquisition on the basis of which the mentioned tools decide about the mutation risk, the modeled mutation likelihoods might vary considerably so that decision uncertainty appears. The data might be incomplete wrt. patient’s origin, age, type of cancer, family history, etc. In this contribution, we take a first step towards data fusion/cleanup and propose two models to combine data on mutation probabilities for better correlation between personal and family cancer history or between different risk factors.
Prof. Ekaterina Auer is a professor of mathematics at the Department of Electrical Engineering of the University of Applied Sciences in Wismar, Germany, since 2015. She received her diplomas in mathematics and computer science from Ulyanovsk State University, Russia, in 2001 and from the University of Duisburg-Essen, Germany, in 2002. She worked at the Chair of Computer Graphics and Scientific Computing at the University of Duisburg-Essen as a research assistant, receiving her Ph.D. in 2007 and her postdoctoral qualification (habilitation) in 2014. Her main areas of interest are algorithms with result verification and their application to engineering problems (e.g., in biomechanics or in energy systems' simulation); uncertainty quantification and propagation using verified, stochastic, or mixed approaches; verification and validation frameworks including uncertainty visualisation; automated comparison and recommendation of verified software; and application of modern parallelization strategies (e.g., using the GPU) in the mentioned contexts.