Algorithms for Understanding and Fighting Infectious Disease
Name
Hie-brianhie-PhD-EECS-2021-thesis.pdf
Description
Thesis PDF
Size
93.69 MB
Format
Adobe PDF
Checksum (MD5)
7e7376eaddac70f7d35e0a963a7a2edc
Author(s)
Hie, Brian Lance
Advisor(s)
Berger, Bonnie A.
Date Issued
June 2021
Publisher
Massachusetts Institute of Technology
Abstract
Infectious disease is a persistent and substantial threat to human health, with consequences that include widespread mortality, suffering, and economic disruption. This thesis presents several algorithmic advances that, when coupled with biotechnologies for data collection and perturbation, are aimed at understanding infectious disease and using this knowledge to fight it. First, this thesis develops geometric algorithms that enable a panoramic understanding of the systems biology of the human immune system and of infectious pathogens at single-cell resolution. Next, this thesis will show how state-of-the-art Bayesian machine learning can explore complex biological spaces to search for new therapies that fight infectious disease. Finally, this thesis develops neural language models that can predict how pathogens mutate to evade human immunity, potentially enabling more broadly effective vaccines and therapies. Taken together, this thesis outlines a highly interdisciplinary, algorithmic approach to infectious disease research, with broader implications for computation and biology more generally.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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In Copyright - Educational Use Permitted
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