Applying machine learning to high-dimensional proteomics datasets for the identification of Alzheimer’s disease biomarkers
Abstract Purpose 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. Met...
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| Format: | Article |
| Language: | English |
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BMC
2025-03-01
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| Series: | Fluids and Barriers of the CNS |
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| Online Access: | https://doi.org/10.1186/s12987-025-00634-z |
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| author | Christoffer Ivarsson Orrelid Oscar Rosberg Sophia Weiner Fredrik D. Johansson Johan Gobom Henrik Zetterberg Newton Mwai Lena Stempfle |
| author_facet | Christoffer Ivarsson Orrelid Oscar Rosberg Sophia Weiner Fredrik D. Johansson Johan Gobom Henrik Zetterberg Newton Mwai Lena Stempfle |
| author_sort | Christoffer Ivarsson Orrelid |
| collection | DOAJ |
| description | Abstract Purpose 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 ( $$A\beta ^-T^-$$ A β - T - or $$A\beta ^+T^+$$ A β + 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. |
| format | Article |
| id | doaj-art-bd96dd68efbc4d96b4bdad4305b555d1 |
| institution | OA Journals |
| issn | 2045-8118 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | BMC |
| record_format | Article |
| series | Fluids and Barriers of the CNS |
| spelling | doaj-art-bd96dd68efbc4d96b4bdad4305b555d12025-08-20T01:57:48ZengBMCFluids and Barriers of the CNS2045-81182025-03-0122111810.1186/s12987-025-00634-zApplying machine learning to high-dimensional proteomics datasets for the identification of Alzheimer’s disease biomarkersChristoffer Ivarsson Orrelid0Oscar Rosberg1Sophia Weiner2Fredrik D. Johansson3Johan Gobom4Henrik Zetterberg5Newton Mwai6Lena Stempfle7Computer Science and Engineering, Chalmers University of Technology and University of GothenburgComputer Science and Engineering, Chalmers University of Technology and University of GothenburgDepartment of Psychiatry and Neurochemistry, The Sahlgrenska Academy at the University of GothenburgComputer Science and Engineering, Chalmers University of Technology and University of GothenburgDepartment of Psychiatry and Neurochemistry, The Sahlgrenska Academy at the University of GothenburgDepartment of Psychiatry and Neurochemistry, The Sahlgrenska Academy at the University of GothenburgComputer Science and Engineering, Chalmers University of Technology and University of GothenburgComputer Science and Engineering, Chalmers University of Technology and University of GothenburgAbstract Purpose 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 ( $$A\beta ^-T^-$$ A β - T - or $$A\beta ^+T^+$$ A β + 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.https://doi.org/10.1186/s12987-025-00634-zAlzheimer’s diseaseProteomicsMass spectrometryHigh-dimensional dataBiomarkersMachine learning |
| spellingShingle | Christoffer Ivarsson Orrelid Oscar Rosberg Sophia Weiner Fredrik D. Johansson Johan Gobom Henrik Zetterberg Newton Mwai Lena Stempfle Applying machine learning to high-dimensional proteomics datasets for the identification of Alzheimer’s disease biomarkers Fluids and Barriers of the CNS Alzheimer’s disease Proteomics Mass spectrometry High-dimensional data Biomarkers Machine learning |
| title | Applying machine learning to high-dimensional proteomics datasets for the identification of Alzheimer’s disease biomarkers |
| title_full | Applying machine learning to high-dimensional proteomics datasets for the identification of Alzheimer’s disease biomarkers |
| title_fullStr | Applying machine learning to high-dimensional proteomics datasets for the identification of Alzheimer’s disease biomarkers |
| title_full_unstemmed | Applying machine learning to high-dimensional proteomics datasets for the identification of Alzheimer’s disease biomarkers |
| title_short | Applying machine learning to high-dimensional proteomics datasets for the identification of Alzheimer’s disease biomarkers |
| title_sort | applying machine learning to high dimensional proteomics datasets for the identification of alzheimer s disease biomarkers |
| topic | Alzheimer’s disease Proteomics Mass spectrometry High-dimensional data Biomarkers Machine learning |
| url | https://doi.org/10.1186/s12987-025-00634-z |
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