![]() Conclusions: A disease-specific variant classifier outperforms state-of-the-art genome-wide tools for rare missense variants in inherited cardiac conditions (), highlighting broad opportunities for improved pathogenicity prediction through disease specificity. Francesco Mazzarotto, Paz Tayal, Rachel Buchan, William Midwinter, Alicja Wilk, Nicola Whiffin, Risha Govind, Erica Mazaika, Antonio De Marvao. Variants classified as disease-causing are associated with both disease status and clinical severity, including a 21% increased risk (95% confidence interval 11–29%) of severe adverse outcomes by age 60 in patients with hypertrophic cardiomyopathy. CardioBoost obtains excellent accuracy (cardiomyopathies 90.2% arrhythmias 91.9%) for variants classified with >90% confidence, and increases the proportion of variants classified with high confidence more than twofold compared with existing tools. Erica Lee Mazaika (Erica Lee Petrushka, Erica Petroshka) 40s Drums, PA View Cell Phone Number View Background Report. ![]() Results: CardioBoost has high global discrimination accuracy (precision recall area under the curve 0.91 for cardiomyopathies 0.96 for arrhythmias), outperforming existing tools (4–24% improvement). Erica Lee Mazaikas phone number, Drums, PA address and more on Whitepages, the most trusted online directory with contact information for over 90 of US adults. ![]() We assessed CardioBoost’s ability to discriminate known pathogenic from benign variants, prioritize disease-associated variants, and stratify patient outcomes. Methods: We developed a disease-specific variant classifier, CardioBoost, that estimates the probability of pathogenicity for rare missense variants in inherited cardiomyopathies and arrhythmias. We hypothesized that incorporating disease-specific information would improve tool performance. State-of-the-art machine learning variant prioritization tools are imprecise and ignore important parameters defining gene–disease relationships, e.g., distinct consequences of gain-of-function versus loss-of-function variants. Abstract: Purpose: Accurate discrimination of benign and pathogenic rare variation remains a priority for clinical genome interpretation.
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