Abstract
The study of a DNA variation’s impact on an individual’s health status is known as variation interpretation. An imprecise interpretation may result in incorrect clinical actions that endanger the patient’s health. Despite its obvious importance, variation interpretation remains an unresolved challenge due to the wide dispersion and heterogeneity of the data necessary for interpretation. Conceptual modeling has previously been demonstrated to be an effective solution to define complex domains, achieving precise and consistent representations of dispersed and heterogeneous data. This work presents the results of applying conceptual modeling to define a conceptual model that describes the required data for conducting variation interpretation. This conceptual model represents the primary data dimensions required for the variation interpretation process and how they are related, resulting in a precise domain description that will help make variation interpretation a systematic, explainable, and reproducible procedure. To demonstrate how our conceptual model assists in achieving a more precise and consistent variation interpretation process, examples of its instantiation to represent the data required for evaluating the ACMG-AMP 2015 variation interpretation guidelines criteria are presented.
Supported by ACIF/2021/117, CIPROM/2021/023, INNEST/2021/57, PID2021-123824OB-I00 and PDC2021-121243-I00, MICIN/AEI/10.13039/501100011033 grants.
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Costa, M., García S., A., León, A., Pastor, O. (2023). Comprehensive Representation of Variation Interpretation Data via Conceptual Modeling. In: Sales, T.P., Araújo, J., Borbinha, J., Guizzardi, G. (eds) Advances in Conceptual Modeling. ER 2023. Lecture Notes in Computer Science, vol 14319. Springer, Cham. https://doi.org/10.1007/978-3-031-47112-4_3
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