Assessment of the predictive accuracy of five in silico prediction tools, alone or in combination, and two metaservers to classify long QT syndrome gene mutations

@inproceedings{Leong2015AssessmentOT,
  title={Assessment of the predictive accuracy of five in silico prediction tools, alone or in combination, and two metaservers to classify long QT syndrome gene mutations},
  author={Ivone US Leong and Alexander W Stuckey and Daniel Lai and Jonathan R Skinner and Donald R. Love},
  booktitle={BMC Medical Genetics},
  year={2015}
}
BackgroundLong QT syndrome (LQTS) is an autosomal dominant condition predisposing to sudden death from malignant arrhythmia. Genetic testing identifies many missense single nucleotide variants of uncertain pathogenicity. Establishing genetic pathogenicity is an essential prerequisite to family cascade screening. Many laboratories use in silico prediction tools, either alone or in combination, or metaservers, in order to predict pathogenicity; however, their accuracy in the context of LQTS is… CONTINUE READING
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