2026-09-21T04:27:03Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/125838 2026-02-03 col_2142_5131 col_2142_8888 com_2142_5130 com_2142_8887 com_2142_234

application/pdf en text Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-08-01 The student, Xinbo Wu, accepted the attached license on 2024-07-18 at 17:36. The student, Xinbo Wu, submitted this Thesis for approval on 2024-07-18 at 17:58. This Thesis was approved for publication on 2024-07-19 at 09:30. DSpace SAF Submission Ingestion Package generated from Vireo submission #21147 on 2025-02-04 at 21:26:02 Exosome Protein Language Model eng Exosomes are extracellular vesicles that propagate in the body as a form of cell-to-cell communication, implicated in many diseases such as cancer and neurodegeneration. To understand the impacts of exosomal messages, it is important to determine the message source: the organ system that initially secreted them. To do so, we develop a new technique based on protein language models (PLMs); PLMs with Transformer neural architecture now learn powerful protein representations in a self-supervised manner. Learned protein representations can be used to estimate the source organs of a protein. Using a pre-trained Transformer-based PLM as a feature extractor and fine-tuning a prediction model over the extracted features to predict source organs, yields reasonable predictive accuracy. We apply this new analysis tool to bulk exosomal proteomics data to understand the differences between healthy aging and neurodegenerative disease. 2024-08 Thesis https://hdl.handle.net/2142/125838 Copyright 2024 Xinbo Wu Source identification for exosomal communication via protein language models Wu, Xinbo 2024-07-19 Varshney, Lav R Electrical & Computer Eng Electrical & Computer Engr University of Illinois at Urbana-Champaign M.S. Thesis