Machine learning models combined with biologically informed dimension reduction techniques yielded excellent classification accuracy of underlying muscle perturbations in simulated cardiac twitches.
Machine learning and dimension reduction techniques applied to complete twitch force time courses can accurately classify underlying molecular perturbations in cardiac muscle disease models.
The timing and magnitude of force generation by a muscle depend on complex interactions in a compliant, contractile filament lattice. Perturbations in these interactions can result in cardiac muscle diseases. In this study, we address the fundamental challenge of connecting the temporal features of cardiac twitches to underlying rate constants and their perturbations associated with genetic cardiomyopathies. Current state-of-the-art metrics for characterizing the mechanical consequence of cardiac muscle disease do not utilize information embedded in the complete time course of twitch force. We pair dimension reduction techniques and machine learning methods to classify underlying perturbations that shape the timing of twitch force. To do this, we created a large twitch dataset using a spatially explicit Monte Carlo model of muscle contraction. Uniquely, we modified the rate constants of this model in line with mouse models of cardiac muscle disease and varied mutation penetrance. Ultimately, the results of this study show that machine learning models combined with biologically informed dimension reduction techniques can yield excellent classification accuracy of underlying muscle perturbations.
Asencio et al. (Wed,) conducted a other in Genetic cardiomyopathies. Machine learning and dimension reduction techniques was evaluated on Classification accuracy of underlying muscle perturbations. Machine learning models combined with biologically informed dimension reduction techniques yielded excellent classification accuracy of underlying muscle perturbations in simulated cardiac twitches.