Abstract

Exosomes are extracellular vesicles that propagate in the body as a form a 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 differences between healthy aging and neurodegenerative disease.

Original languageEnglish (US)
Title of host publication2022 IEEE 32nd International Workshop on Machine Learning for Signal Processing, MLSP 2022
PublisherIEEE Computer Society
ISBN (Electronic)9781665485470
DOIs
StatePublished - 2022
Event32nd IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2022 - Xi'an, China
Duration: Aug 22 2022Aug 25 2022

Publication series

NameIEEE International Workshop on Machine Learning for Signal Processing, MLSP
Volume2022-August
ISSN (Print)2161-0363
ISSN (Electronic)2161-0371

Conference

Conference32nd IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2022
Country/TerritoryChina
CityXi'an
Period8/22/228/25/22

Keywords

  • exosomes
  • protein language model

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Signal Processing

Fingerprint

Dive into the research topics of 'Source Identification for Exosomal Communication via Protein Language Models'. Together they form a unique fingerprint.

Cite this