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Biomedical Engineering and Bioengineering Commons™
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Articles 1 - 2 of 2
Full-Text Articles in Biomedical Engineering and Bioengineering
Identification And Characterization Of Two Novel Kcnh2 Mutations Contributing To Long Qt Syndrome, Anthony Owusu-Mensah, Jacqueline Treat, Joyce Bernardi, Ryan Pfeiffer, Robert Goodrow, Bright Tsevi, Victoria Lam, Michel Audette, Jonathan M. Cordeiro, Makarand Deo
Identification And Characterization Of Two Novel Kcnh2 Mutations Contributing To Long Qt Syndrome, Anthony Owusu-Mensah, Jacqueline Treat, Joyce Bernardi, Ryan Pfeiffer, Robert Goodrow, Bright Tsevi, Victoria Lam, Michel Audette, Jonathan M. Cordeiro, Makarand Deo
Electrical & Computer Engineering Faculty Publications
We identified two different inherited mutations in KCNH2 gene, or human ether-a-go-go related gene (hERG), which are linked to Long QT Syndrome. The first mutation was in a 1-day-old infant, whereas the second was in a 14-year-old girl. The two KCNH2 mutations were transiently transfected into either human embryonic kidney (HEK) cells or human induced pluripotent stem-cell derived cardiomyocytes. We performed associated multiscale computer simulations to elucidate the arrhythmogenic potentials of the KCNH2 mutations. Genetic screening of the first and second index patients revealed a heterozygous missense mutation in KCNH2, resulting in an amino acid change (P632L) in the …
Domain Adaptive Federated Learning For Multi-Institution Molecular Mutation Prediction And Bias Identification, W. Farzana, M. A. Witherow, I. Longoria, M. S. Sadique, A. Temtam, K. M. Iftekharuddin
Domain Adaptive Federated Learning For Multi-Institution Molecular Mutation Prediction And Bias Identification, W. Farzana, M. A. Witherow, I. Longoria, M. S. Sadique, A. Temtam, K. M. Iftekharuddin
Electrical & Computer Engineering Faculty Publications
Deep learning models have shown potential in medical image analysis tasks. However, training a generalized deep learning model requires huge amounts of patient data that is usually gathered from multiple institutions which may raise privacy concerns. Federated learning (FL) provides an alternative to sharing data across institutions. Nonetheless, FL is susceptible to a few challenges including inversion attacks on model weights, heterogenous data distributions, and bias. This study addresses heterogeneity and bias issues for multi-institution patient data by proposing domain adaptive FL modeling using several radiomics (volume, fractal, texture) features for O6-methylguanine-DNA methyltransferase (MGMT) classification across multiple institutions. The proposed …