Unsupervised Machine Learning Using Cerebrospinal Fluid Proteomics for Understanding Parkinson’s Disease Progression

Authors

  • Lubna Mahmoud Abu Zohair School of Mathematical and Computer Science, Heriot-Watt University, Dubai, United Arab Emirates
  • Hind Zantout School of Mathematical and Computer Science, Heriot-Watt University, Dubai, United Arab Emirates
  • Marta Vallejo School of Mathematical and Computer Science, Heriot-Watt University, Edinburgh, United Kingdom
  • Md Azher Uddin School of Mathematical and Computer Science, Heriot-Watt University, Dubai, United Arab Emirates

DOI:

https://doi.org/10.1609/aaaiss.v6i1.36033

Abstract

This study explores the potential of advanced, context-aware machine learning algorithms, such as autoencoders, to represent longitudinal cerebrospinal fluid proteomic data, enabling the objective discovery of two patient strata with significance.
AAAI Summer Symposium 2025 Proceedings Cover

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Published

2025-08-01

How to Cite

Abu Zohair, L. M., Zantout, H., Vallejo, M., & Uddin, M. A. (2025). Unsupervised Machine Learning Using Cerebrospinal Fluid Proteomics for Understanding Parkinson’s Disease Progression. Proceedings of the AAAI Symposium Series, 6(1), 72–74. https://doi.org/10.1609/aaaiss.v6i1.36033

Issue

Vol. 6 No. 1: Proceedings of the 2025 AAAI Summer Symposium Series

Section

Context-Awareness in Cyber-Physical Systems