Machine-learning based strategy identifies a robust protein biomarker panel for Alzheimer’s disease in cerebrospinal fluid
Bibliographic record
Abstract
BACKGROUND: The complex pathogenesis of Alzheimer's disease (AD) has resulted in limited current biomarkers for its classification and diagnosis, necessitating further investigation into reliable universal biomarkers or combinations. METHODS: In this work, we collect multiple CSF proteomics datasets and build a universal diagnose model by SVM-RFECV method combined with equal sample size and standard normalization design. The model was training in 297_CSF and then test the effect in other datasets. RESULTS: Utilizing machine learning, we identify a 12-protein panel from cerebrospinal fluid proteomic datasets. The universal diagnosis model demonstrated strong diagnostic capability and high accuracy across ten different AD cohorts across different countries and different detection technologies. These proteins involved in various biological processes related to AD and shows a tight correlation with established AD pathogenic biomarkers, including amyloid-β, tau/p-tau, and the Montreal Cognitive Assessment score. The high accuracy in the model may due to multiple protein combination based on comprehensive pathogenesis and different AD progress. Furthermore, it effectively differentiates AD from mild cognitive impairment (MCI) and other neurodegenerative disorders, especially the frontotemporal dementia (FTD), which share similar pathogenesis as AD. CONCLUSION: This study highlights a high accuracy, robustness and compatibility model of 12-protein panel whose detection is even based on label-free, TMT and DIA mass spectrometry or ELISA technologies, implicating its potential prospect in clinical application.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".