Please use this identifier to cite or link to this item:
http://hdl.handle.net/2445/202079
Title: | Choosing Variant Interpretation Tools for Clinical Applications: Context Matters |
Author: | Aguirre, Josu Padilla, Natàlia Özkan, Selen Riera, Casandra Feliubadaló, Lídia Cruz, Xavier de la |
Keywords: | Diagnòstic molecular Medicina personalitzada Economia de la salut Molecular diagnosis Personalized medicine Medical economics |
Issue Date: | 24-Jul-2023 |
Publisher: | MDPI AG |
Abstract: | Pathogenicity predictors are computational tools that classify genetic variants as benign or pathogenic; this is currently a major challenge in genomic medicine. With more than fifty such predictors available, selecting the most suitable tool for clinical applications like genetic screening, molecular diagnostics, and companion diagnostics has become increasingly challenging. To address this issue, we have developed a cost-based framework that naturally considers the various components of the problem. This framework encodes clinical scenarios using a minimal set of parameters and treats pathogenicity predictors as rejection classifiers, a common practice in clinical applications where low-confidence predictions are routinely rejected. We illustrate our approach in four examples where we compare different numbers of pathogenicity predictors for missense variants. Our results show that no single predictor is optimal for all clinical scenarios and that considering rejection yields a different perspective on classifiers. |
Note: | Reproducció del document publicat a: https://doi.org/10.3390/ijms241411872 |
It is part of: | International Journal of Molecular Sciences, 2023, vol. 24, num. 14 |
URI: | http://hdl.handle.net/2445/202079 |
Related resource: | https://doi.org/10.3390/ijms241411872 |
ISSN: | 1422-0067 |
Appears in Collections: | Articles publicats en revistes (Institut d'lnvestigació Biomèdica de Bellvitge (IDIBELL)) |
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