Key result
A machine learning model trained on sequence- and structure-based features successfully predicted loss-of-function versus gain-of-function effects of missense variants in voltage-gated sodium and calcium channels with an ROC of 0.85.
Effect estimate: ROC 0.85
A novel machine learning model accurately predicts loss-of-function versus gain-of-function effects of missense variants in voltage-gated sodium and calcium channels, which may aid in interpreting genetic variants in channelopathies.
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May aid variant interpretation in channelopathies; leaves open human validation before clinical adoption.
Heyne et al. (2020) studied Channelopathies (SCN and CACNA1 gene variants) (n=1,521). Machine learning model (FunNCion) vs. Other variant prediction tools (CADD, PolyPhen-2, MPC) was evaluated on Prediction of loss-of-function (LOF) versus gain-of-function (GOF) variant effects (ROC 0.85). A machine learning model trained on sequence- and structure-based features successfully predicted loss-of-function versus gain-of-function effects of missense variants in voltage-gated sodium and calcium channels with an ROC of 0.85.
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