TY - GEN
T1 - Source Identification for Exosomal Communication via Protein Language Models
AU - Wu, Xinbo
AU - Hanganu, Alexandru
AU - Hoshino, Ayuko
AU - Varshney, Lav R.
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - exosomes
KW - protein language model
UR - http://www.scopus.com/inward/record.url?scp=85142679524&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85142679524&partnerID=8YFLogxK
U2 - 10.1109/MLSP55214.2022.9943418
DO - 10.1109/MLSP55214.2022.9943418
M3 - Conference contribution
AN - SCOPUS:85142679524
T3 - IEEE International Workshop on Machine Learning for Signal Processing, MLSP
BT - 2022 IEEE 32nd International Workshop on Machine Learning for Signal Processing, MLSP 2022
PB - IEEE Computer Society
T2 - 32nd IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2022
Y2 - 22 August 2022 through 25 August 2022
ER -