| Autor: |
Aguirre J; Research Unit in Clinical and Translational Bioinformatics, Vall d'Hebron Institute of Research (VHIR), Universitat Autònoma de Barcelona, P/Vall d'Hebron, 119-129, 08035 Barcelona, Spain., Padilla N; Research Unit in Clinical and Translational Bioinformatics, Vall d'Hebron Institute of Research (VHIR), Universitat Autònoma de Barcelona, P/Vall d'Hebron, 119-129, 08035 Barcelona, Spain., Özkan S; Research Unit in Clinical and Translational Bioinformatics, Vall d'Hebron Institute of Research (VHIR), Universitat Autònoma de Barcelona, P/Vall d'Hebron, 119-129, 08035 Barcelona, Spain., Riera C; Research Unit in Clinical and Translational Bioinformatics, Vall d'Hebron Institute of Research (VHIR), Universitat Autònoma de Barcelona, P/Vall d'Hebron, 119-129, 08035 Barcelona, Spain., Feliubadaló L; Hereditary Cancer Program, Program in Molecular Mechanisms and Experimental Therapy in Oncology (Oncobell), IDIBELL, Catalan Institute of Oncology, 08908 L'Hospitalet de Llobregat, Spain.; Centro de Investigación Biomédica en Red de Cáncer (CIBERONC), 28929 Madrid, Spain., de la Cruz X; Research Unit in Clinical and Translational Bioinformatics, Vall d'Hebron Institute of Research (VHIR), Universitat Autònoma de Barcelona, P/Vall d'Hebron, 119-129, 08035 Barcelona, Spain.; Institució Catalana de Recerca i Estudis Avançats (ICREA), 08010 Barcelona, Spain. |
| Abstrakt: |
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. |
