810-762-3191

Applications:
Bioinformatics, Learning Health Systems, Medical Imaging, Medical Informatics, Nanotechnology, Precision Medicine
Methodologies:
Artificial Intelligence, Computational Tools for Data Science, Dynamical Models, Machine Learning, Mathematics

Yasser Aboelkassem

Assistant Professor

College of Innovation and Technology

In this project, we use multi-scale models coupled with machine learning algorithms to study cardiac electromechanic coupling. The approach spans out the molecular, Brownian, and Langevin dynamics of the contractile (sarcomeric proteins) mechanism of cardiac cells and up-to-the finite element analysis of the tissue and organ levels. In this work, a novel surrogate machine learning algorithm for the sarcomere contraction is developed. The model is trained and established using in-silico data-driven dynamic sampling procedures implemented using our previously derived myofilament mathematical models.

Multi-scale Machine Learning Modeling of Cardiac Electromechanics Coupling

Multi-scale Machine Learning Modeling of Cardiac Electromechanics Coupling