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The dissertation presents a significant advancement in the field of cardiac cellular systems and molecular signature systems by employing machine learning and generative artificial intelligence techniques. These methodologies are systematically characterized and applied to address critical challenges in these domains. A novel computational model is developed, which combines machine learning tools and multi-physics models. The main objective of this model is to accurately predict complex cellular dynamics, taking into account the intricate interactions within the cardiac cellular system. Furthermore, a comprehensive framework based on generative adversarial networks (GANs) is proposed. This framework is designed to generate synthetic data that faithfully represents an in-vitro cardiac cellular system. The generated data can be used to enhance the understanding and analysis of the system’s behavior. Additionally, a novel AI approach is formulated, which integrates deep learning and GAN techniques for Raman characterization. This approach enables efficient detection of multi-analyte mixtures by leveraging the power of deep learning algorithms and the generation of synthetic data through GANs. Overall, the integration of machine learning, generative artificial intelligence, and multi-physics modeling provides valuable insights and tools for precise prediction and efficient detection in cardiac cellular systems and molecular signature systems.
Wu, Ziqian, "System-Characterized Artificial Intelligence Approaches for Cardiac cellular systems and Molecular Signature analysis" (2023). Dartmouth College Ph.D Dissertations. 185.