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

Lubna Mahmoud Abu Zohair; Hind Zantout; Marta Vallejo; Md Azher Uddin

doi:10.1609/aaaiss.v6i1.36033

August 1, 2025Proceedings of the AAAI Symposium Series

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

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Key Points

The study identifies two distinct patient strata crucial for understanding disease progression.
Using advanced machine learning algorithms like autoencoders significantly enhances data analysis.
This method represents complex cerebrospinal fluid proteomics for insightful findings.
Research findings may point to new pathways for monitoring Parkinson's disease.

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Implication

This analysis demonstrates the use of autoencoders to reveal two patient strata, suggesting new insights into progression in Parkinson’s disease.

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.

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Cite This Study

Zohair et al. (2025) studied this question.

synapsesocial.com/papers/68c1a90554b1d3bfb60e1e80 https://doi.org/10.1609/aaaiss.v6i1.36033

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

Key Points

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

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

Key Points

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

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