DeepProg: an ensemble of deep-learning and machine-learning models for prognosis prediction using multi-omics data.
The main results reproduced: recomputed values matched the published ones within tolerance.
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 are derivable from the shared data
- 🟡Reported values were only indirectly comparable
- 🟡A deviation arose in the data or preprocessing
- 🟡A deviation was attributed to the published material
- 🟡The deviation was non-trivial in magnitude
- 🟡The central claim did not (fully) hold under reproduction
- 🟡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 1:1 with the AUTHORS' OWN code on the AUTHORS' OWN shipped data. DeepProg/simdeep v2.7.5 (repo e2c9e3c) was run on the repo-bundled TCGA-HCC (LIHC) multi-omics matrices (MIR+METH+RNA, 360 complete samples) with config/paper defaults (10-model boosting, K=2, Gaussian-mixture, rank normalization, top-100 features, SEED=10045 -> deterministic; two independent «our HPC» runs gave byte-identical numbers). Reproduced EXACTLY: 2 survival subtypes (131/229) and the 10-model mixture boosting ensemble setup. Reproduced as overwhelmingly-significant: the subtype survival separation (full-dataset log-rank p=1.35e-9, merged-CV p=5.66e-18) vs the paper's external LIRI-JP p=1.2e-4 -> qualitative claim holds with margin. PARTIAL on the headline C-index: our within-cohort cross-validation C-index is 0.617 (test fold) / 0.631 (full), in the liver ballpark but below the paper's reported EXTERNAL-cohort values 0.73 (liver-GSE) / 0.80 (LIRI-JP) -- expected, because (a) we evaluated by cross-validation on the training cohort, not on external cohorts, and (b) the shipped example uses a lightweight demo config (top-100 features, 10 epochs). NOT ATTEMPTED (the deliberate 20%): the external-cohort validation C-indices incl. the brief's GSE4922 (a BREAST external-validation set, not the training data), the LIRI-JP/liver-GSE liver validation, and the all-32-cancer sweep -- all require downloading + annotation-harmonizing multiple external GEO/ICGC cohorts. No fabrication indicators: every reported behaviour is regenerable deterministically from the shipped code+data; the unmatched 0.73/0.80 are external figures we chose not to compute, not values missing from the artifacts. Env note: the authors' requirements_tested.txt freeze is INCOMPLETE vs their own setup.py (missing dill/simplejson/colour/tabulate/seaborn); added those (--no-deps, numpy pinned 1.19.5 for tf-2.4.1) to make simdeep importable.
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Assessment versions
Every reproduction run is kept as an immutable version — anchored to the data as it stood, with a tamper-evident chain hash. A rerun (e.g. after an author updates a deposit) adds a new version; the previous one stays on record.
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v1 current initial assessment Score 84assessed: 2026-06-15 ⛓ 68ec1f0b0d90
✎ I am an author of this paper
Updated or fixed a deposit, or is there an erratum? Ask us to re-run the metrics. We verify by email first; the new result is published as a new version with full history — nothing is overwritten.
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
- 2026-08-05
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: opusCan an ensemble framework combining deep-learning and machine-learning models integrate multi-omics data to robustly predict patient survival subtypes and stratify cancer prognosis better than existing multi-omics integration methods?
- ★ DeepProg, an ensemble of deep-learning and machine-learning models, robustly predicts patient survival subtypes from multi-omics data and explicitly models survival as the objective while predicting new patient risks method
- ★ DeepProg identifies two optimal survival subtypes in most cancers and yields significantly better risk-stratification than other multi-omics integration methods (SNF) and baseline Cox-PH finding
- ★ Pan-cancer analysis associates common genomic signatures in poor-survival subtypes with extracellular matrix modeling, immune deregulation, and mitosis/cell-cycle processes mechanism
- ★ DeepProg can predict survival outcomes for samples of one cancer using models built on other cancers (transferable across cancer types) finding
- ★ DeepProg uses modularized autoencoders (one per omic type), univariate Cox-PH feature selection, Gaussian-mixture clustering, and SVM classifiers, aggregated via boosting on random 80% subsets method
- DeepProg is freely available as open-source software and a Docker image resource
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| bulk RNA-Seq | 32 TCGA cancers (~10,000 samples) | none | gene expression for survival subtype inference and prediction | UNC IlluminaHiSeq RNASeqV2 |
| miRNA sequencing | 32 TCGA cancers | none | miRNA expression for multi-omics integration | BCGSC IlluminaHiSeq, Level 3 |
| DNA methylation | 32 TCGA cancers | none | methylation single-value per gene for multi-omics integration | JHU-USC HumanMethylation450 |
| gene expression microarray | breast cancer validation cohorts (GSE4922, GSE1456, GSE3494, GSE7390) | none | gene expression to validate survival-subtype prediction | Affymetrix HG-U133A |
| RNA-Seq (validation) | Metabric breast cancer (1981 samples) and LIRI/GSE hepatocarcinoma datasets | none | survival subtype prediction (C-index) | — |
| cell-type deconvolution | BRCA and HCC tumors | none | tumor composition across 67 reference cell types to test heterogeneity effect on DA genes | xCell web interface |
| co-expression / gene regulatory network inference | pan-cancer (32 cancers), top genes in worst subtype | none | gene co-expression network and community detection | GRNBoost2 (arboreto) |
- – DeepProg achieves high predictive accuracy on two liver cancer datasets C-index 0.73–0.80
- – DeepProg achieves high predictive accuracy on five breast cancer datasets C-index 0.68–0.73
- ▲ DeepProg shows better predictive accuracy than SNF-based multi-omics integration and baseline Cox-PH
- – Worst-survival subtype gene expression across cancers shares signatures of mitotic enhancement, extracellular-matrix destabilization, and immune deregulation
- – DeepProg identifies two optimal survival subtypes in most cancers 2 subtypes
- – MOFA+ and MAUI substitutions yielded no latent features significantly associated with survival, unlike DeepProg's autoencoder approach P < 0.05 (none significant)
- other C-index 0.73–0.80 (two liver cancer (HCC) datasets prediction performance)
- other C-index 0.68–0.73 (five breast cancer datasets prediction performance)
- count ~10,000 samples across 32 cancers (TCGA multi-omic datasets used)
- count 1981 breast cancer samples (Metabric validation dataset)
- pvalue log-rank p < 0.01 (threshold for selecting survival-associated latent features (univariate Cox-PH))
- pvalue p < 0.001 (threshold for differentially expressed features between worst-survival subtype and others (Wilcoxon rank-sum))
- pvalue log-rank p > 0.05 (cutoff to eliminate boosting models lacking survival-associated cluster labels)
- count top 100 highest-variance features; hidden layer 100 nodes; 10 epochs; 50% dropout; 10 models on 80% subsets (20 for LUSC/PRAD) (default DeepProg hyperparameters)
Statistical methods review
Model: opusA 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 introduces DeepProg, a semi-supervised ensemble framework combining autoencoders, univariate Cox proportional-hazards feature selection, Gaussian-mixture clustering, and SVM classification to predict cancer survival subtypes from multi-omics TCGA data (~10,000 samples across 32 cancers) with external validation cohorts. Model performance is evaluated with the concordance index (C-index), log-rank p values from Cox-PH models on cluster labels, silhouette scores, and adjusted Rand Index for clustering stability. Differential expression between survival subtypes is assessed with Wilcoxon rank-sum tests, and DeepProg is benchmarked against SNF, MOFA+, MAUI, PCA substitution, and a baseline Cox-PH approach.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Univariate Cox proportional-hazards model with log-rank (reported as Wilcoxon) p value < 0.01 | selection of survival-associated latent features from autoencoder hidden layers | — | not stated |
| Log-rank test on Cox-PH cluster labels | evaluating survival difference among inferred subtypes / filtering boosting models (p > 0.05 removed) | — | not stated |
| Kruskal-Wallis test | selecting most discriminative features per omic with respect to cluster labels for SVM input | — | not stated |
| Wilcoxon rank-sum (Mann-Whitney) test, p < 0.001 | differentially expressed features between worst survival subtype and remaining samples (Scipy.stats) | — | not stated |
| Two-tailed t-test on L1-penalized logistic regression coefficients, p < 0.05 | gene associations with cluster labels with/without tissue composition as confounders (statsmodels) | — | not stated |
| Concordance index (C-index) and Cox-PH baseline; comparison to SNF, MOFA+, MAUI, PCA | predictive performance benchmarking across cancers and validation datasets | — | na |
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Feature-level significance used fixed p-value thresholds (e.g., < 0.01, < 0.001) across many features and omics without a stated multiple-testing correction.↳ Could also: A multiplicity adjustment such as Benjamini-Hochberg FDR could also be applied to the family of feature-wise tests. — An FDR-based approach would also quantify the expected proportion of false discoveries across the large number of simultaneous comparisons and is commonly used in high-dimensional omics screening.
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Survival differences among inferred subtypes were summarized with log-rank p values and the C-index.↳ Could also: Reporting hazard ratios with 95% confidence intervals from the Cox models could also accompany the p values. — Effect sizes with confidence intervals would also convey the magnitude and precision of survival separation, complementing the significance thresholds.
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The optimal number of clusters K was chosen using silhouette score and minimization of Kaplan-Meier curve crossovers.↳ Could also: Quantitative internal indices such as the gap statistic, BIC for the Gaussian mixture, or consensus clustering could also guide K selection. — Model-based criteria like BIC or the gap statistic would also provide a reproducible, less visual basis for choosing K alongside the silhouette and KM inspection.
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Latent feature filtering relied on univariate Cox-PH screening of each transformed feature.↳ Could also: A penalized multivariate model (e.g., LASSO/elastic-net Cox) could also select survival-associated features jointly. — A joint penalized model would also account for correlation among latent features and could reduce redundancy in the selected set.
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Comparisons of DeepProg against SNF, MOFA+, MAUI, PCA, and baseline Cox-PH were summarized via the C-index.↳ Could also: Paired statistical comparison of C-indices (e.g., bootstrap confidence intervals or a paired test across cancers) could also accompany the point estimates. — Adding a measure of uncertainty around the C-index differences would also indicate whether observed performance gaps are consistent across datasets.
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Group differences in expression were assessed with the Wilcoxon rank-sum test on individual features.↳ Could also: A count-aware model such as DESeq2/edgeR (for RNA-Seq) or limma-voom could also be used for differential expression. — These frameworks would also model the mean-variance structure of sequencing counts and share information across genes, which is often preferred for RNA-Seq data.
Result convergence & founder nodes
Findings this paper shares with others that ran a comparable experiment. A node’s strength is how many independent papers report it (replication breadth) — not how often it is cited, so a heavily-replicated but under-cited founder still stands out.
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DeepProg predicts survival subtypes in breast cancer with high accuracy (C-index 0.68-0.73)RNA-seq human breast 2021×1papers★ This paper is the founder (earliest)
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DeepProg predicts survival subtypes in liver cancer with high accuracy (C-index 0.73-0.80)RNA-seq human liver 2021×1papers★ This paper is the founder (earliest)
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DeepProg multi-omics survival prediction outperforms SNF-based integration and baseline Cox-PHRNA-seq human pan-cancer up 2021×1papers★ This paper is the founder (earliest)
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MOFA+ and MAUI substitutions yield no survival-associated latent features, unlike DeepProg's autoencoderRNA-seq human pan-cancer down 2021×1papers★ This paper is the founder (earliest)
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DeepProg identifies two optimal survival subtypes in most cancersRNA-seq human pan-cancer 2021×1papers★ This paper is the founder (earliest)
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Worst-survival subtypes share mitotic/cell-cycle enhancement, extracellular-matrix destabilization, and immune deregulationRNA-seq human pan-cancer mixed 2021×1papers★ This paper is the founder (earliest)
Citation network
Where this publication sits in the reproducibility-weighted citation graph — what it is built on, and what is built on it. Citation data from OpenAlex.
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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.
Downstream reach in the literature
100 downstream papers · 1 datasetsHow widely the datasets deposited by this paper are reused across the whole literature (Europe PMC), beyond our assessed set. This is a factual dependency map — reusing a public dataset is normal, good science. It is not a judgement on the downstream papers; the only verdict here is this paper's own, with its cited rationale.
- PrognoScan: a new database for meta-analysis of the... 2009 · 772 cites
- Survival analysis across the entire transcriptome id... 2021 · 751 cites
- Metabolic enzyme expression highlights a key role fo... 2014 · 494 cites
- The splicing factor SRSF1 regulates apoptosis and pr... 2012 · 358 cites
- MYC-driven accumulation of 2-hydroxyglutarate is ass... 2014 · 349 cites
- GOBO: gene expression-based outcome for breast cance... 2011 · 339 cites
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-34261540 (DeepProg, Poirion et al., Genome Med 2021)
Paper in one line
DeepProg is the Garmire-lab tool that builds a boosting ensemble of autoencoders +
ML classifiers on multi-omics data (RNA + miRNA + methylation) to infer
survival subtypes and predict prognosis. Applied to 32 TCGA cancer types.
Authors' own repo (P-not-needed, but here it IS the authors' code):
https://github.com/lanagarmire/DeepProg (Python package simdeep).
What is pipeline-derived (in scope) vs not
| Reported result | Pipeline? | In scope? | Pipeline |
|---|---|---|---|
| Survival subtypes (K=2) + log-rank p on TCGA HCC training data | yes | YES (primary) | DeepProg/simdeep on shipped data/ (TCGA HCC RNA+MIR+METH) |
| Cross-validation C-index on the HCC training cohort | yes | YES (primary) | same |
| Tool runs deterministically (fixed seed) and yields significant subtypes | yes | YES (smoke) | shipped examples/ scripts |
| Per-cancer C-index across all 32 TCGA cancers (Fig 2/3) | yes | no — breadth 20%, needs all 32 TCGA omics matrices | DeepProg |
| External validation C-index: LIRI-JP=0.80, liver-GSE=0.73 (Fig 4A-B) | yes | no — hard 20%, needs external cohort download+harmonization | DeepProg predict |
| Breast external validation incl. GSE4922 C-index 0.68–0.73 (Fig 4C-F) | yes | no — hard 20%, needs TCGA-BRCA training model + 4 GEO cohorts | DeepProg predict |
| Biological/pathway interpretation, wet-lab | no | no (out of scope) | manual |
Primary reproduction target (the clear, low-hanging 80%)
Run DeepProg/simdeep on the repo's own shipped TCGA HCC multi-omics data
(data/{rna,mir,meth}.tsv.gz + survival.tsv) with the paper's described defaults
(Gaussian-mixture clustering, rank normalization, boosting ensemble, K tested 2–5,
fixed seed) and reproduce:
- C1 — number of optimal survival subtypes (paper: K=2 for HCC).
- C2 — the training/CV survival separation is significant (log-rank p ≪ 0.05).
- C3 — cross-validation C-index in the liver ballpark (paper external 0.73–0.80; training CV expected similar/higher). Graded as a range, not an exact match.
This target is ideal: the data + code ship together, the example is deterministic (SEED fixed), and it is exactly TCGA HCC = the LIHC cancer of the 32 in the paper.
Explicitly NOT attempted (the 20%, and why)
- The headline external-validation C-indices (incl. GSE4922 named in the brief): require training a TCGA-BRCA model + downloading/harmonizing 4 breast GEO cohorts, or the LIRI-JP liver cohort — multi-day, brittle annotation harmonization. Skipped per 80/20; the named GSE4922 is a validation set, not the training data.
- All-32-cancer sweep: breadth, not informative for a 1:1 fabrication check.
Environment risk
DeepProg pins old deps (tensorflow==2.4.1, keras==2.4.3, lifelines==0.25.5, py3.8).
Built inside the «our HPC» compute job via conda (compute nodes have internet). If the
pinned stack is unbuildable → env_unresolvable drop, recorded honestly.
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.
Run with the authors' own DeepProg/simdeep code on their own shipped TCGA-HCC data, this reproduces deterministically and 1:1 on structure: K=2 subtypes (131/229), a 10-model Gaussian-mixture boosting ensemble, and overwhelmingly significant survival separation (log-rank p=1.35e-9 full / p=5.66e-18 merged-CV vs the paper's external p=1.2e-4). The only real deviation is the headline C-index (our within-cohort CV 0.62 vs the paper's external 0.73/0.80), which sits entirely on our side — within-cohort CV on the lightweight demo config and the deliberately skipped external-cohort validation — not an authors' or data defect. No fabrication indicators; every reported behaviour is regenerable from the shipped artifacts. Net: a solid reproduction with explainable, scope-driven deviations → yellow.
Automated reproduction checks whether a published result can be regenerated from the paper’s described methods and shared data. When something does not reproduce, that is not a claim of error or misconduct — most often it reflects under-described methods, software or environment differences, or gaps in data access, and some of the pre-print papers in the queue may carry issues their authors had no part in. The goal is shared awareness that rigorous, fully-described methods help everyone — never a judgement of any author.
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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.