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Related Experiment Video

Updated: Jul 21, 2025

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Choosing Variant Interpretation Tools for Clinical Applications: Context Matters.

Josu Aguirre1, Natàlia Padilla1, Selen Özkan1

  • 1Research 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.

International Journal of Molecular Sciences
|July 29, 2023
PubMed
Summary

Selecting the best computational tools for classifying genetic variants is challenging. A new cost-based framework helps choose appropriate pathogenicity predictors for clinical applications, considering rejected low-confidence predictions.

Keywords:
classification with rejectionclinical variant interpretationcost modelshealthcare costsin silico toolsmolecular diagnosticspathogenicity predictionpersonalized medicine

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Area of Science:

  • Genomic Medicine
  • Bioinformatics
  • Computational Biology

Background:

  • Classifying genetic variants as benign or pathogenic is crucial for genomic medicine.
  • Numerous pathogenicity predictors exist, making tool selection for clinical applications difficult.
  • Existing tools face challenges in accuracy and applicability across diverse clinical scenarios.

Purpose of the Study:

  • To develop a cost-based framework for selecting optimal pathogenicity predictors.
  • To provide a systematic approach for evaluating computational tools in clinical settings.
  • To address the challenge of choosing the most suitable predictor for genetic screening and diagnostics.

Main Methods:

  • Developed a cost-based framework encoding clinical scenarios with minimal parameters.
  • Treated pathogenicity predictors as rejection classifiers, incorporating low-confidence prediction rejection.
  • Compared the performance of different numbers of predictors across various missense variant cases.

Main Results:

  • No single pathogenicity predictor demonstrated universal optimality across all clinical scenarios.
  • The inclusion of prediction rejection significantly altered the perspective on classifier performance.
  • The cost-based framework offers a nuanced approach to predictor selection.

Conclusions:

  • The optimal choice of pathogenicity predictors is context-dependent and scenario-specific.
  • Incorporating a rejection strategy enhances the utility of pathogenicity predictors in clinical practice.
  • The proposed framework provides a valuable tool for informed decision-making in genomic medicine.