Predicting the Functional Impact of KCNQ1 Variants of Unknown Significance [Original Articles]

Conclusions—
Although a plethora of tools are available for making pathogenicity predictions over a genome-wide scale, previous tools fail to perform in a robust manner when applied to KCNQ1. The contrasting and favorable results for Q1VarPred suggest a promising approach, where a machine-learning algorithm is tailored to a specific protein target and trained with a functionally validated data set to calibrate informatics tools.

Source: Circulation: Cardiovascular Genetics - October 11, 2017 Category: Cardiology Authors: Li, B., Mendenhall, J. L., Kroncke, B. M., Taylor, K. C., Huang, H., Smith, D. K., Vanoye, C. G., Blume, J. D., George, A. L., Sanders, C. R., Meiler, J. Tags: Arrhythmias, Electrophysiology, Computational Biology, Genetics Original Articles Source Type: research