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Assessing performance of pathogenicity predictors using clinically-relevant variant datasets
[article]
2020
biorxiv/medrxiv
pre-print
Purpose: Pathogenicity predictors are an integral part of genomic variant interpretation but, despite their widespread usage, an independent validation of performance using a clinically-relevant dataset has not been undertaken. Methods: We derive two validation datasets: an "open" dataset containing variants extracted from publicly-available databases, similar to those commonly applied in previous benchmarking exercises, and a "clinically-representative" dataset containing variants identified
doi:10.1101/2020.02.06.937169
fatcat:pmq2xqmsu5caha4wuantc3lvsa