Identifying pathogenic variants and annotating them is a major challenge in human genetics, especially for the non-coding ones. Several tools have been developed and used to predict the functional effect of genetic variants. However, the calibration assessment of the predictions has received little attention. Calibration refers to the idea that if a model predicts a group of variants to be pathogenic with a probability P, it is expected that the same fraction P of true positive is found in the observed set. For instance, a well-calibrated classifier should label the variants such that among the ones to which it gave a probability value close to 0.7, approximately 70% actually belong to the pathogenic class. Poorly calibrated algorithms can be misleading and potentially harmful for clinical decision-making. Supplementary information Supplementary data are available at Bioinformatics online.

Calibrating variant-scoring methods for clinical decision making / Benevenuta, Silvia; Capriotti, Emidio; Fariselli, Piero. - In: BIOINFORMATICS. - ISSN 1367-4803. - ELETTRONICO. - 36:24(2020), pp. 5709-5711. [10.1093/bioinformatics/btaa943]

Calibrating variant-scoring methods for clinical decision making

Capriotti, Emidio
;
2020

Abstract

Identifying pathogenic variants and annotating them is a major challenge in human genetics, especially for the non-coding ones. Several tools have been developed and used to predict the functional effect of genetic variants. However, the calibration assessment of the predictions has received little attention. Calibration refers to the idea that if a model predicts a group of variants to be pathogenic with a probability P, it is expected that the same fraction P of true positive is found in the observed set. For instance, a well-calibrated classifier should label the variants such that among the ones to which it gave a probability value close to 0.7, approximately 70% actually belong to the pathogenic class. Poorly calibrated algorithms can be misleading and potentially harmful for clinical decision-making. Supplementary information Supplementary data are available at Bioinformatics online.
2020
Calibrating variant-scoring methods for clinical decision making / Benevenuta, Silvia; Capriotti, Emidio; Fariselli, Piero. - In: BIOINFORMATICS. - ISSN 1367-4803. - ELETTRONICO. - 36:24(2020), pp. 5709-5711. [10.1093/bioinformatics/btaa943]
Benevenuta, Silvia; Capriotti, Emidio; Fariselli, Piero
File in questo prodotto:
File Dimensione Formato  
btaa943.pdf

accesso aperto

Tipo: Versione (PDF) editoriale
Licenza: Licenza per Accesso Aperto. Creative Commons Attribuzione - Non commerciale (CCBYNC)
Dimensione 1.58 MB
Formato Adobe PDF
1.58 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/795418
Citazioni
  • ???jsp.display-item.citation.pmc??? 6
  • Scopus 7
  • ???jsp.display-item.citation.isi??? 3
social impact