Associated Papers
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.
Leong IU et al.
Bmc Medical Genetics, 2015
25967940 (PubMed )
Supplement: 12881_2015_176_MOESM1_ESM.xlsx › KCNQ1_All
A new approach to long QT syndrome mutation detection by Sequenom MassARRAY system.
Allegue C et al.
Electrophoresis, 2010
Spectrum of mutations in long-QT syndrome genes. KVLQT1, HERG, SCN5A, KCNE1, and KCNE2.
Splawski I et al.
Circulation, 2000
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