JUCS - Journal of Universal Computer Science 29(11): 1361-1384, doi: 10.3897/jucs.112797
Towards a Traceable Data Model Accommodating Bounded Uncertainty for DST Based Computation of BRCA1/2 Mutation Probability With Age
Lorenz Gillner‡, Ekaterina Auer‡‡ University of Applied Sciences Wismar, Wismar, Germany
Corresponding author: Lorenz Gillner ( lorenz.gillner@hs-wismar.de ) Corresponding author: Ekaterina Auer ( ekaterina.auer@hs-wismar.de ) © Lorenz Gillner, Ekaterina Auer. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY-ND 4.0). This license allows reusers to copy and distribute the material in any medium or format in unadapted form only, and only so long as attribution is given to the creator. The license allows for commercial use. Citation:
Gillner L, Auer E (2023) Towards a Traceable Data Model Accommodating Bounded Uncertainty for DST Based Computation of BRCA1/2 Mutation Probability With Age. JUCS - Journal of Universal Computer Science 29(11): 1361-1384. https://doi.org/10.3897/jucs.112797 |  |
Abstract
In this paper, we describe the requirements for traceable open-source data retrieval in the context of computation of BRCA1/2 mutation probabilities (mutations in two tumor-suppressor genes responsible for hereditary BReast or/and ovarian CAncer). We show how such data can be used to develop a Dempster-Shafer model for computing the probability of BRCA1/2 mutations enhanced by taking into account the actual age of a patient or a family member in an appropriate way even if it is not known exactly. The model is compared with PENN II and BOADICEA (based on undisclosed data), two established platforms for this purpose accessible online, as well as with our own previous models. A proof-of-concept implementation shows that set-based techniques are able to provide better information about mutation probabilities, simultaneously highlighting the necessity for ground truth data of high quality.
Keywords
HBOC, BRCA1/2, mutation probability, data integration, data fusion, interval analysis, Dempster-Shafer evidence theory, provenance, AI based data mining