CAPICE: a computational method for Consequence-Agnostic Pathogenicity Interpretation of Clinical Exome variations

Abstract excerpt

<h4>ABSTRACT</h4> Exome sequencing is now mainstream in clinical practice, however, identification of pathogenic Mendelian variants remains time consuming, partly because limited accuracy of current computational prediction methods leaves much manual classification. Here we introduce CAPICE, a new machine-learning based method for prioritizing pathogenic variants, including SNVs and short InDels, that outperforms...

Topics

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• Exome
• INDEL Mutation
• Machine Learning
• Molecular Diagnostic Techniques
• Molecular Sequence Annotation
• Reproducibility of Results
• ROC Curve
• Software

Identifiers and source

Literature Corpus work

3952a7e7-dd35-57ce-852c-62057b27078a

DOI

10.1101/19012229