As cardiovascular disease remains the global leading cause of death, there is an urgent need to study the pathophysiology of the heart and to effectively evaluate cardioprotective drugs. Due to the difficulty in studying the human heart in vivo and sourcing human heart tissues, induced pluripotent stem cells (iPSCs) have provided a promising alternative for modeling cardiac diseases and evaluating candidate drugs. In recent years, computational methods, including machine learning, have given rise to a new class of tools to evaluate cardiac function more rapidly and comprehensively.
In this dissertation, I develop and apply computational tools to probe the function of human iPSC-derived cardiac models (Aim 1), apply machine learning methods in the context of cardiomyocyte disease phenotyping and cardioactive drug profiling (Aim 2), and develop a pipeline for deep learning-driven cardiac fibroblast phenotypic drug discovery (Aim 3).
| Identifer | oai:union.ndltd.org:columbia.edu/oai:academiccommons.columbia.edu:10.7916/1w0k-5j62 |
| Date | January 2024 |
| Creators | Kim, Youngbin |
| Source Sets | Columbia University |
| Language | English |
| Detected Language | English |
| Type | Theses |
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