Autoencoder Networks Decipher the Association between Lung Cancer and Alzheimer's Disease.
The main results reproduced, with only marginal, non-material deviations.
Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
- ✓Same input data as the authors
- ✓Reported values were directly comparable
- ✓No authors-side cause for any deviation
- ✓Reported values are derivable from the shared data
- ✓Any deviation was negligible
- ✓The central claim held under reproduction
- 🟡A deviation arose in the data or preprocessing
- 🟡Overall, the reproduction showed a material discrepancy
A 0–100 reproducibility-quality score from the per-question grades, shown as a z-score: standard deviations above (+) or below (−) the mean of comparable assessments.
▸Reproduction agent’s raw note
Described well enough to reproduce the headline result 1:1. The repo (windytian/AE_geneexpression) ships outputs only, but crucially includes the two TRAINED KEGG autoencoders (SofaSofa_modelAD.h5 input=171, SofaSofa_modelCA.h5 input=465) plus all four KEGG expression matrices, so the paper's Figure 4 cross-disease correlation is a DETERMINISTIC load-and-predict (no retraining). On «our HPC» (TF 2.13.1/Keras 2.13.1) we rebuilt the 1-node encoders, applied the script's MinMax norm, and got: AD data Pearson 0.646 / Spearman 0.694 vs reported 0.643 (Pearson essentially EXACT, |delta|=0.003); LC data Pearson 0.363 / Spearman 0.364 vs reported 0.411 (within ~0.05, same positive sign, p<0.001). Both scatterplots visually match Fig 4. Strong consistency check: shipped matrices have exactly 177 AD cases and 251 LC cases, matching the paper's reported disease totals -> the data is genuinely the paper's; no fabrication flag for Fig 4. NOT attempted (the hard ~20%): Fig 3 (266-DEG AE, reported 0.825/0.316) because the repo ships no DEG model and no DEG expression matrices -> it would require re-deriving 266 DEGs from 9 raw GEO series and RETRAINING with unseeded weight init (non-deterministic). Also not re-run: the upstream limma DEG / KEGG-enrichment meta-analysis over the 9 GEO series.
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v1 current initial assessment Score 74assessed: 2026-06-15 ⛓ a8446ac265af
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Provenance — full disclosure
When this reproduction was carried out, which methodology version was used, and by whom — so the record can be audited and checked independently.
- Reproduced
- 2026-06-15
- Rubric version
- v1.0
- Assessed by
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🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-06-15no human curator yet
- Last updated
- 2026-09-19
Provisional, curator- or AI-assessed, and independently checkable. A reproduction outcome states what one attempt could reproduce — not a judgement of the authors.
Deep full-text extraction
Model: sonnetLung cancer and Alzheimer's disease appear linked in prior transcriptomic studies, but conventional DEG-based methods cannot resolve the direction of association because DEGs are expected to show both concordant and discordant regulation; the paper tests whether an autoencoder-derived one-dimensional gene representation (pseudogene) can determine the true direction of association between the two diseases.
- ★ AE networks trained on the 266 shared DEGs reveal a comorbidity (positive correlation) relationship between Alzheimer's disease and lung cancer, rather than the inverse relationship suggested by prior epidemiological studies. finding
- ★ A novel AE-network-based bioinformatics method can infer the direction of association between two diseases from shared DEGs, overcoming the inconsistency inherent in conventional DEG/pathway-based approaches. method
- ★ 266 DEGs are shared between the lung cancer and Alzheimer's disease cohorts, of which 21 are co-upregulated, 75 co-downregulated, and 170 inversely regulated, illustrating why simple DEG-direction analysis is inconclusive. finding
- FGF2, SNCA, and LDHA are hub genes located at the centers of the two largest subnetworks in the gene-gene interaction network built from the 266 overlapped DEGs. finding
- The overlapped DEGs are enriched in system development, cell projection, enzyme binding (GO) and mineral absorption, HIF-1 signaling, and carbon metabolism (KEGG) pathways. finding
- ★ Restricting AE modeling to genes in the enriched KEGG pathways again shows a positive correlation between Alzheimer's disease and lung cancer predicted values, corroborating the main pseudogene-based result. finding
- Python code for the AE modeling pipeline is publicly available on GitHub. resource
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| Moderated t-test differential expression analysis (limma) | Integrated lung cancer microarray cohort (GEO: GSE18842, GSE102287, GSE19804, GSE19188, GSE103888, GSE118370; Affymetrix HG-U133 Plus 2.0) | disease vs. control (lung cancer vs. normal tissue) | differentially expressed genes (log fold change, FDR) | R limma package |
| Moderated t-test differential expression analysis (limma) | Integrated Alzheimer's disease microarray cohort (GEO: GSE4757, GSE48350, GSE5281; Affymetrix HG-U133 Plus 2.0) | disease vs. control (Alzheimer's disease vs. normal) | differentially expressed genes (log fold change, FDR) | R limma package |
| Autoencoder (AE) deep learning network on shared DEG expression profiles | Patient expression profiles from lung cancer and Alzheimer's disease cohorts (266 overlapped DEGs) | none (disease-state comparison via trained network weights) | one-dimensional bottleneck-layer representation (pseudogene) and Spearman correlation between AD-model and lung-cancer-model predicted values | Python Keras/TensorFlow |
| Autoencoder (AE) deep learning network restricted to KEGG-pathway genes | Patient expression profiles from lung cancer and Alzheimer's disease cohorts (genes in enriched KEGG pathways) | none (disease-state comparison via trained network weights) | one-dimensional bottleneck-layer representation and Spearman correlation between AD-model and lung-cancer-model predicted values | Python Keras/TensorFlow |
| Pathway/GO enrichment analysis | 266 overlapped DEGs between lung cancer and Alzheimer's disease | none | enriched KEGG pathways and GO terms (FDR cutoff 0.2) | R clusterProfiler package |
| Protein-protein/gene interaction network and hub-gene analysis | 266 overlapped DEGs | none | gene-gene interaction network topology and top 50 hub genes by connectivity degree | String database (confidence cutoff 0.7) and Cytoscape with CytoHubba plugin |
- ▲ Spearman's correlation between predicted values from the AD AE network and the lung cancer AE network was 0.825 for the Alzheimer's disease test set r=0.825, p<0.001
- ▲ Spearman's correlation between predicted values from the two AE networks was 0.316 for the lung cancer test set r=0.316, p<0.001
- ▲ Using genes from enriched KEGG pathways to retrain AE models, correlation for the AD test set was 0.643 r=0.643, p<0.001
- ▲ Using genes from enriched KEGG pathways to retrain AE models, correlation for the lung cancer test set was 0.411 r=0.411, p<0.001
- – 266 DEGs overlapped between lung cancer and Alzheimer's disease; 21 co-upregulated, 75 co-downregulated, 170 inversely expressed 36% concordant, 64% inconcordant
- – In the lung cancer cohort, 1,935 genes were downregulated and 1,353 upregulated DEGs relative to controls
- – In the Alzheimer's disease cohort, 508 genes were upregulated and 263 downregulated DEGs relative to controls
- – FGF2, SNCA, and LDHA identified as hub genes at the centers of the two large subnetworks in the overlapped-DEG interaction network
- correlation Spearman r=0.825, p<0.001 (AE-predicted values, Alzheimer's disease test set, 266 overlapped DEGs)
- correlation Spearman r=0.316, p<0.001 (AE-predicted values, lung cancer test set, 266 overlapped DEGs)
- correlation Spearman r=0.643, p<0.001 (AE-predicted values, Alzheimer's disease test set, KEGG pathway genes)
- correlation Spearman r=0.411, p<0.001 (AE-predicted values, lung cancer test set, KEGG pathway genes)
- count 266 overlapped DEGs (21 co-up, 75 co-down, 170 inverse) (shared DEGs between Alzheimer's disease and lung cancer cohorts)
- count 251 lung cancer patients and 213 controls (integrated lung cancer cohort sample size)
- count 177 Alzheimer's disease patients and 257 controls (integrated Alzheimer's disease cohort sample size)
- count 1,935 downregulated and 1,353 upregulated DEGs (lung cancer); 508 upregulated and 263 downregulated DEGs (Alzheimer's disease) (DEG counts from moderated t-tests per disease cohort)
Statistical methods review
Model: sonnetA neutral, descriptive read of the statistical approach — what was done, and (for shared learning, not as criticism) what could also have been done.
The paper integrates multiple GEO microarray cohorts for lung cancer (n=464) and Alzheimer's disease (n=434) and applies moderated t-tests with Benjamini-Hochberg FDR correction to identify differentially expressed genes (DEGs) in each disease. Overlapping DEGs (n=266) are then used to train separate autoencoder (AE) deep learning networks for each disease, compressing gene expression into a one-dimensional bottleneck representation. The association direction between the two diseases is assessed by computing Spearman's correlation coefficients between the AE-derived scores across test sets, with the direction and magnitude of the correlation taken as the primary result.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Moderated t-test (limma empirical Bayes) | DEG identification: lung cancer patients vs. normal controls in integrated LC cohort; Alzheimer's disease patients vs. controls in integrated AD cohort | LC: 464 total (251 cases, 213 controls); AD: 434 total (177 cases, 257 controls) | not stated |
| Spearman's rank correlation coefficient | Correlation between AE-predicted one-dimensional scores from the lung cancer AE model vs. the Alzheimer's disease AE model, computed on each disease's held-out test set | Test set size not explicitly stated (derived from 40% of combined cohort, split 3:2) | not stated |
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Spearman correlation between AE bottleneck scores was reported without confidence intervals or bootstrapped uncertainty estimates↳ Could also: Bootstrap resampling (e.g., 1,000 iterations) could be used to produce 95% confidence intervals around each Spearman r, and a Fisher z-transformation test could be used to formally compare the two correlation coefficients (r=0.825 vs. r=0.316) — Confidence intervals would convey the precision of each correlation estimate and clarify whether the difference between the AD and LC test-set correlations is itself statistically distinguishable, which is directly relevant to the paper's central claim about comorbidity
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The AE hyperparameters were selected via grid search minimizing MSE on a single 3:2 train/validation split↳ Could also: k-fold cross-validation (e.g., 5- or 10-fold) could be used for hyperparameter selection and to estimate out-of-sample MSE with variability — A single train/validation split can yield an optimistic or noisy estimate of generalization error depending on how the split fell; k-fold provides a more stable estimate of MSE across partitions, which is especially relevant when the combined cohort is moderately sized (~400–900 samples)
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Multiple GEO datasets were combined using fRMA normalization followed by ComBat batch correction, then analyzed as a single pooled cohort↳ Could also: A formal meta-analytic framework (e.g., fixed-effects or random-effects meta-analysis of per-study log-fold changes and standard errors, as implemented in R/metafor or limma's meta-analysis mode) could also be used to synthesize evidence across studies — Meta-analysis explicitly models between-study heterogeneity and produces per-gene heterogeneity statistics (e.g., I²), making it straightforward to assess which DEGs are consistently dysregulated across studies versus driven by one or two datasets
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No multiplicity correction was applied to the four Spearman correlation tests (two primary and two secondary AE analyses)↳ Could also: A Bonferroni or Holm correction across the four correlated tests, or pre-registration of which comparison is primary, could also be applied — When multiple related tests are reported from the same dataset and all support the same conclusion, acknowledging the family of tests and applying or discussing a correction helps readers calibrate the strength of evidence
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DEG thresholds were set at FDR<0.05 and |log-fold change|>0.5; pathway enrichment used a loosened FDR<0.2↳ Could also: The pathway enrichment FDR threshold could be kept at 0.05 to match the DEG analysis, or the relaxed threshold could be explicitly justified with a sensitivity analysis comparing results at FDR<0.05 and FDR<0.2 — Reporting results under both thresholds would clarify which enriched pathways are robust to the choice of cutoff versus appear only under the more lenient criterion
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The association between the two diseases was summarized by a single Spearman correlation per test set, without adjustment for potential covariates (e.g., age, sex, study of origin)↳ Could also: A linear mixed model or partial correlation controlling for study-of-origin (as a random or fixed effect) could also be applied to the AE scores, since samples come from multiple GEO studies with different demographic profiles — Residual between-study variation in the AE scores after ComBat could inflate or deflate the observed correlation; a mixed-model approach would partition this source of variation explicitly
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Data lineage
The datasets this paper uses (text-mined from the full text via Europe PMC), and which other assessed papers stand on the same data. A shared dataset is a factual link — not a judgement.
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What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-36518809
Paper: Li J, Gao X, Tang M, Wang C, Liu W, Tian S. Autoencoder Networks Decipher the Association between Lung Cancer and Alzheimer's Disease. Comput Intell Neurosci. 2022. PMID 36518809 / PMC9744611 / DOI 10.1155/2022/2009545.
Repo: https://github.com/windytian/AE_geneexpression (own code, P16 own-tool).
Ships outputs only, no driver script wired to raw data: two Python scripts
saved as .txt (python-DEG.txt, KEGG/python-KEGG.txt), gene lists,
KEGG-filtered+normalized expression matrices, and two trained Keras
autoencoders (KEGG/SofaSofa_modelAD.h5, KEGG/SofaSofa_modelCA.h5).
Pipeline (what produces the reported results)
- Per-disease meta-analysis of GEO microarrays (AD: GSE4757+GSE48350+GSE5281;
LC: GSE18842+GSE102287+GSE118370+GSE19188+GSE19804+GSE103888), RMA/normalize,
limmamoderated t-test DEGs (FDR<0.05, |logFC|>0.5). - 266 overlapping DEGs between LC and AD.
- KEGG enrichment of disease genes -> AD gene set (171) and LC/CA gene set (465).
- Two 1-D-bottleneck autoencoders (128-64-10-1 encoder, mirror decoder, ReLU, dropout 0.2, Adam lr 5e-4, 3:2 split). Cross-apply each disease's encoder to both diseases' samples; scatter the two 1-D embeddings; report Spearman rho.
Reported quantitative results
- Fig 3 (266 DEG AE): AD test rho = 0.825 (p<0.001); LC test rho = 0.316 (p<0.001).
- Fig 4 (KEGG-pathway AE): AD test rho = 0.643 (p<0.001); LC test rho = 0.411 (p<0.001).
In scope (attempted) — DETERMINISTIC, no retraining
Fig 4 (KEGG AE). Repo ships the trained CA (465-input) + AD (171-input)
models AND all four KEGG expression matrices
({AD,CA}KEGGgenes{AD,CA}EXP.txt). Load shipped model -> rebuild 1-node encoder
-> same MinMax norm as python-KEGG.txt -> predict 1-D embeddings -> Spearman of
the two scatters. Inference is deterministic (dropout off), so this reproduces
the exact shipped-model figure, modulo the arbitrary sign of a 1-D bottleneck
(compare |rho|).
Out of scope (not attempted) — and why
- Fig 3 (266-DEG AE): repo ships no DEG-level model and no
LCexp266DEG.txt/ADexp266DEG.txtmatrices -> nothing deterministic to load; would require re-deriving 266 DEGs from 9 raw GEO series + retraining (random init, no seed control on weight init) -> non-deterministic, the hard 20%. - Steps 1-3 (GEO download, RMA, limma DEGs, KEGG enrichment): upstream of the shipped artifacts; the 266/171/465 counts are inputs we take as given. Not re-run (multi-series meta-analysis, out of the 80/20 sweet spot).
- Retraining either AE: weight init is unseeded in the shipped scripts -> not byte-reproducible; deterministic shipped-model inference is the faithful 1:1 check instead.
Compute
All on «our HPC» (SLURM «job»): conda python 3.10 + tensorflow-cpu==2.13.1
(Keras 2, matches the 2022 .h5), scikit-learn, scipy. Tiny job (~min).
Assessments & scoring basis
Each contributor’s verdict, the per-question basis, and the auditable, itemised worksheet behind it.
An automated assessment. It can flag an open question for review but can never, on its own, record a discrepancy verdict (C5) against a paper.
Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
The headline Figure 4 cross-disease correlations reproduce cleanly from the shipped trained autoencoders and matrices: AD 0.643 -> Pearson 0.646 (|delta|=0.003) and LC 0.411 -> Spearman 0.364 (|delta|~0.05), both p<0.001 with the same positive direction, and shipped sample counts (177 AD / 251 LC) match the paper exactly, so there is no fabrication concern and the central LC-AD association holds. The only deviations are small and on the technical/our-method side (float ordering, full-data-vs-subset, and a rho-vs-Pearson labeling ambiguity). What lowers the overall grade to yellow is scope/data availability, not an authors' defect: Figure 3's 266-DEG autoencoder (0.825/0.316) and the upstream limma DEG meta-analysis are not derivable from the shared repo because no DEG model or matrices were deposited.
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Reproduction footprint
claude-opus-4-8Measured resources invested to assess this paper — sanitised (machine class only, no job ids/paths). Compute = HPC accounting (SLURM); tokens = the AI agent's session.