Machine learning on unbiased proteomics of cerebrospinal fluid uncovers differential molecular signatures of Alzheimer’s disease and Normal Pressure Hydrocephalus
Matthijs B de Geus, Chao‐Yi Wu, Hiroko H Dodge, Shannon N Leslie, Weiwei Wang, TuKiet Lam, Angus C Nairn, Pia Kivisäkk, Steven E Arnold, Becky C Carlyle · 2025
Read Machine learning on unbiased proteomics of cerebrospinal fluid uncovers differential molecular signatures of Alzheimer’s disease and Normal Pressure Hydrocephalus by Matthijs B de Geus, Chao‐Yi Wu, Hiroko H Dodge, Shannon N Leslie, Weiwei Wang, TuKiet Lam, Angus C Nairn, Pia Kivisäkk, Steven E Arnold, Becky C Carlyle (2025) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-11714358.txt.
Description
Matthijs de Geus and co-authors used machine learning on spinal fluid proteins to tell two brain conditions apart in 2025. Alzheimer disease and normal pressure hydrocephalus can look alike in clinic, which makes diagnosis hard. Past protein studies of the second condition were small and shallow.
Fluid samples came from a neurology clinic's diagnostic taps. Patients were classed as controls, Alzheimer, or hydrocephalus by symptoms, response to a large fluid tap, and amyloid status. A deep protein scan measured thousands of proteins at once. Random forest models then searched for protein signatures that split the classes in pairs. Top features were ranked, and pathway tests showed which body processes were raised or lowered in each condition.
Catalog record
| CREATOR | Matthijs B de Geus, Chao‐Yi Wu, Hiroko H Dodge, Shannon N Leslie, Weiwei Wang, TuKiet Lam, Angus C Nairn, Pia Kivisäkk, Steven E Arnold, Becky C Carlyle |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 3 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |