A 3-fold kernel approach for characterizing Late Onset Alzheimer’s Disease
Bibliographic record
Abstract
Summary The purpose of this study is to identify a global and robust signature characterizing Alzheimer’s Disease (AD). Two public GWAS datasets were analyzed considering a 3-fold kernel approach, based on SNPs, Genes and Pathways analysis, and two binary classifications tasks were addressed: cases@controls and APOE4 task. In the SNP signature of the ADNI-1 and ADNI-2 datasets, chromosome 19 and 20 reached high classification accuracy. In addition, the functional characterization of ADNI-1 and ADNI-2 SNP signatures found enriched the same pathway (i.e., Neuroactive ligand-receptor interaction), with GRM7 gene in common with both. TOMM40 was confirmed linked to AD pathology by SNP, gene and pathway-based analyses in ADNI-1. Using this 3-fold kernel approach, a peculiar signature of SNPs, genes and pathways has been highlighted in both datasets. Based on these significant results, we retain such approach a valuable tool to elucidate the heritable susceptibility to AD but also to other similar complex diseases.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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".