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Bioengineer
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Revolutionizing the Future of Immunotherapy Design
- Dr. Natasa Miskov-Zivanov, an assistant professor at the University of Pittsburgh, merges computational engineering and immunotherapy through her project funded by the NSF CAREER Award.
- Her research aims to revolutionize the design of immune cells, particularly lymphocytes, for advanced therapies against cancer using AI techniques and knowledge graphs.
- Immunotherapy like CAR T cell therapy has shown success in blood cancers but faces challenges in treating solid tumors, prompting the need for new receptor configurations and complex cell engineering strategies.
- Miskov-Zivanov's AI-driven system integrates scientific literature and data to recommend optimal lymphocyte designs, enhancing the efficiency of immunotherapy development.
- Her computational framework automates labor-intensive processes in cell engineering, accelerating the design cycle and revealing novel pathways for therapeutic advancements.
- By leveraging large language models and neural networks, the system interprets scientific papers and experimental data to simulate and screen potential cell designs before laboratory testing.
- Improved prompting techniques enable precise data extraction from biomedical literature, reducing time and resources in immunotherapy research and design.
- Miskov-Zivanov converts science-derived data into knowledge graphs, enhancing predictive accuracy using graph neural networks to evaluate immunotherapeutic cell configurations.
- Her educational initiatives focus on equipping future researchers with tools for complex biomedical challenges, emphasizing the integration of structured knowledge and data-driven learning models.
- The project seeks to develop and test numerous immunotherapeutic cell designs, with the potential to revolutionize cancer treatment by improving therapies for solid tumors and contributing algorithmic innovations in biomedical research.
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