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dc.contributor.advisorBarzilay, Regina
dc.contributor.authorMurphy, Sean
dc.date.accessioned2024-08-21T18:55:38Z
dc.date.available2024-08-21T18:55:38Z
dc.date.issued2024-05
dc.date.submitted2024-07-10T12:59:47.191Z
dc.identifier.urihttps://hdl.handle.net/1721.1/156308
dc.description.abstractThe targetable delivery of therapeutic nanoparticles remains a significant challenge in modern medicine, particularly due to the complexity, time, and expense involved in experimental design and optimization for cell-specific applications. To address this, NOCAP (Nanoparticle Optimization and Cell Affinity Prediction) was developed, a computational framework designed to (i) predict the affinities between nanoparticles and gene expression signatures of cancer cells and (ii) optimize nanoparticle formulations for specific targets. NOCAP successfully predicts cellular affinity for previously unseen cancer cell lines. The findings demonstrate the potential of machine learning to streamline the rational selection of target-specific nanoparticle drug delivery systems, paving the way for more efficient and precise therapeutic interventions.
dc.publisherMassachusetts Institute of Technology
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
dc.rightsCopyright retained by author(s)
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.titleToward Cell-Specific Nanoparticle Delivery Systems
dc.typeThesis
dc.description.degreeS.M.
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
mit.thesis.degreeMaster
thesis.degree.nameMaster of Science in Electrical Engineering and Computer Science


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