Applying machine learning to high-dimensional proteomics datasets for the identification of Alzheimer's disease biomarkers
Journal article, 2025
This study explores the application of machine learning to high-dimensional proteomics datasets for identifying Alzheimer’s disease (AD) biomarkers. AD, a neurodegenerative disorder affecting millions worldwide, necessitates early and accurate diagnosis for effective management.
Methods
We leverage Tandem Mass Tag (TMT) proteomics data from the cerebrospinal fluid (CSF) samples from the frontal cortex of patients with idiopathic normal pressure hydrocephalus (iNPH), a condition often comorbid with AD, with rare access to both lumbar and ventricular samples. Our methodology includes extensive data preprocessing to address batch effects and missing values, followed by the use of the Synthetic Minority Over-sampling Technique (SMOTE) for data augmentation to overcome the small sample size. We apply linear, and non-linear machine learning models, and ensemble methods, to compare iNPH patients with and without biomarker evidence of AD pathology ( Aβ−T − or Aβ+T +) in a classification task.
Results
We present a machine learning workflow for working with high-dimensional TMT proteomics data that addresses their inherent data characteristics. Our results demonstrate that batch effect correction has no or minor impact on the models’ performance and robust feature selection is critical for model stability and performance, especially in the high-dimensional proteomics data setting for AD diagnostics. The results further indicated that removing features with missing values produced stronger models than imputing them, and the batch effect had minimal impact on the models Our best-performing disease-progression detection model, a random forest, achieves an AUC of 0.84 (± 0.03).
Conclusion
We identify several novel protein biomarkers candidates, such as FABP3 and GOT1, with potential diagnostic value for AD pathology detection, suggesting the necessity of different biomarkers for AD diagnoses for patients with iNPH, and considering different biomarkers for ventricular and lumbar CSF samples. This work underscores the importance of a meticulous machine learning process in enhancing biomarker discovery. Our study also provides insights in translating biomarkers from other central nervous system diseases like iNPH, and both ventricular and lumbar CSF samples for biomarker discovery, providing a foundation for future research and clinical applications.
Proteomics
High-dimensional data
Alzheimer's disease
Biomarkers
Machine learning
Mass spectrometry
Feature selection
Author
Christoffer Ivarsson Orrelid
University of Gothenburg
Oscar Rosberg
University of Gothenburg
Sophia Weiner
University of Gothenburg
Fredrik Johansson
Data Science and AI 3
University of Gothenburg
Johan Gobom
Sahlgrenska University Hospital
University of Gothenburg
Henrik Zetterberg
University of Wisconsin Madison
Hong Kong Center for Neurodegenerative Diseases
Sahlgrenska University Hospital
University College London (UCL)
Newton Mwai Kinyanjui
University of Gothenburg
Chalmers, Computer Science and Engineering (Chalmers), Data Science and AI
Lena Stempfle
University of Gothenburg
Chalmers, Computer Science and Engineering (Chalmers), Data Science and AI
Fluids and Barriers of the CNS
20458118 (eISSN)
Vol. 22 1 23Subject Categories (SSIF 2025)
Other Clinical Medicine
Other Medical Engineering
Artificial Intelligence
DOI
10.1186/s12987-025-00634-z
PubMed
40033432