Machine learning-based DNA microarray analysis for disease detection using the MICRO-AI framework.
Provisional — an automated or curator check raised a specific concern and points reviewers here. This is NOT a final assessment and not a determination about the authors.
The main result did not reproduce in this reproduction attempt. Where our recomputation produced values that differ from the published ones, those discrepancies are listed below. This is a single automated attempt — not peer review and not a finding of error or misconduct — and differences can also arise from data access, undocumented parameters or the computing environment. The verdict can be contested via “report an error”.
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
- 🔴A deviation arose in the data or preprocessing
- 🔴A deviation was attributed to the published material
- 🔴Reported values were not (fully) derivable from the shared data
- 🔴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
MISMATCH with high fabrication concern, confirmed on TWO datasets with COMPLETE 5-seed x 5-fold leakage-free CV runs of the AUTHORS' OWN micro_ai.py (commit ad27cab) on the REAL GEO data. Dataset dimensions match exactly for both (C1,C9). Every performance claim fails by huge margins: GSE2034 Acc 72.7% vs 95.8%, AUC 0.691 vs 0.978, F1 0.481 vs 0.955, MCC 0.301 vs 0.914; GSE7390 Acc 63.1% vs 97.5%, AUC 0.573 vs 0.986, F1 0.285 vs 0.973, MCC 0.052 vs 0.949. Both reproduced accuracies sit AT/BELOW the majority-class baseline (75.9% / 68.7%); GSE7390 per-seed AUC reaches 0.52 and per-seed MCC is negative (worse than chance). Two independent fabrication indicators beyond performance: (a) Table 6 lists MAD-filter survivors 8,456/7,892 but the authors' own MAD filter passes 16,712 for BOTH datasets; (b) the reported fixed 127/98 selected genes are not produced -- the shipped RFECV varies 127-5000 / 98-4500 per fold. The repo ships NO script reproducing any table (main runs on np.random synthetic data). Reported 0.978/0.986 AUC exceed every published result on these well-studied cohorts (literature ceiling ~0.65-0.70). Labels: GSE2034 'bone relapses' (only public binary phenotype), GSE7390 'e.dmfs' distant-metastasis event -- both honest public prognosis labels; conclusion robust to label choice. NOT attempted (80/20): TCGA-BRCA, GSE62254, E-MTAB-365, GSE9891, Table-7 external baselines. All grades AUTOMATED and PROVISIONAL; a human reviewer decides.
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Assessment versions
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v1 current initial assessment Score 22assessed: 2026-06-16 ⛓ 043d97a33831
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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
- 2026-06-22
- Rubric version
- v1.0
- Assessed by
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👤 1 human curator(s) · Level L2 2026-06-16
- 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: sonnetThe paper investigates whether an integrated, end-to-end machine learning framework (MICRO-AI) combining attention-weighted feature selection with heterogeneous ensemble classification can overcome the high dimensionality, small sample size, batch effects and class imbalance that limit DNA microarray-based disease diagnosis, achieving higher and more clinically deployable diagnostic accuracy than existing single-component or deep learning approaches.
- ★ MICRO-AI's attention-weighted feature fusion reduces dimensionality by over 99% (from ~20,000 to ~127 genes) without loss of biological significance finding
- ★ Heterogeneous ensemble (GBM+RF+SVM) with SLSQP-based adaptive weight optimisation achieves 5-7% higher accuracy than individual classifiers and 0.8% improvement over equal weighting finding
- ★ The integrated preprocessing pipeline (quantile normalisation, ComBat correction, weighted KNN imputation) contributes a 1.7% accuracy improvement over uncorrected data finding
- ★ MICRO-AI outperforms 10 state-of-the-art methods by 1.2-7.5% accuracy across six benchmark datasets from GEO, TCGA and ArrayExpress finding
- ★ MICRO-AI trains 2.4-6.0x faster than deep learning alternatives with an average training time of 52.3 s finding
- ★ MICRO-AI (Microarray Classification and Recognition using Artificial Intelligence) is a modular four-module framework: preprocessing, attention-weighted feature selection, ensemble classification and clinical integration resource
- Attention scores for gene prioritisation are computed from deterministic statistical moment embeddings (mean, variance, skewness, kurtosis) and mutual information with class labels, not end-to-end gradient learning method
- The framework produces calibrated probability estimates via isotonic regression with expected calibration error minimisation for clinical decision support mechanism
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| DNA microarray gene expression profiling / classification | Human samples: breast cancer, gastric cancer, ovarian cancer, leukaemia (6 datasets from GEO, TCGA, ArrayExpress) | none (disease diagnosis/classification, no experimental perturbation) | disease class prediction / classification accuracy, sensitivity, specificity, F1, MCC, AUC-ROC | — |
- – Average classification accuracy across six datasets 96.8%
- – Average sensitivity 95.2%
- – Average specificity 97.4%
- – F1-score and Matthews correlation coefficient F1=96.0%, MCC=0.928
- – Area under ROC curve 0.983
- ▼ Dimensionality reduction from feature selection >99% (~20,000 to ~127 genes)
- ▲ Accuracy gain from adaptive ensemble weighting vs individual classifiers / equal weighting 5-7% / 0.8%
- ▲ Accuracy improvement from preprocessing pipeline vs uncorrected data 1.7%
- mean 96.8% accuracy (average classification accuracy across six benchmark datasets)
- mean 95.2% sensitivity, 97.4% specificity, 96.0% F1-score (average diagnostic performance metrics)
- other MCC = 0.928 (Matthews correlation coefficient, average across datasets)
- other AUC-ROC = 0.983 (average area under ROC curve)
- fold_change 1.2-7.5% higher accuracy (comparison vs 10 state-of-the-art methods)
- fold_change 2.4-6.0x faster execution (training time vs deep learning alternatives (avg 52.3 s))
- other >99% dimensionality reduction (~20,000 to ~127 genes) (attention-weighted feature selection result)
- count 2321 total samples across 6 datasets, 3 repositories, 4 cancer types (overall validation cohort size)
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.
MICRO-AI is a machine learning classification framework evaluated entirely through classification performance metrics (accuracy, sensitivity, specificity, F1-score, MCC, AUC-ROC) on six pre-existing benchmark genomic datasets totalling 2321 samples across four cancer types. No traditional hypothesis tests are reported; comparative benchmarking against ten published methods is conducted by direct point-estimate metric comparison. Results are presented as averages across datasets without measures of dispersion, confidence intervals, or statistical significance assessment of cross-method differences.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Recursive feature elimination with cross-validation (RFECV) with attention-weighted scoring | Gene/feature subset identification across all six datasets | 155–1097 per dataset; 2321 total across six datasets | not stated |
| Gene Ontology (GO) enrichment analysis | Biological validation of selected gene signatures (mentioned in abstract contributions) | — | not stated |
| Cross-validation-based classification performance evaluation (fold count not specified in available text) | Primary model evaluation for all six datasets across GBM, RF, SVM ensemble | 155–1097 per dataset | not stated |
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Cross-method performance comparisons report point-estimate accuracy differences (1.2–7.5% improvement over ten methods) without any significance testing↳ Could also: A Wilcoxon signed-rank test or McNemar's test applied to per-dataset paired performance scores could also assess whether observed differences are statistically distinguishable from chance variation — Significance tests on paired per-dataset results distinguish systematic gains from dataset-specific fluctuation, which is especially informative when only six datasets are available
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Classification metrics are reported as means across six datasets with no measure of spread↳ Could also: Reporting per-dataset results alongside their mean ± SD, or computing bootstrap confidence intervals on the aggregate metrics, could also convey variability — Dispersion measures help readers assess consistency: a high mean with wide variance across datasets signals different generalisability than a high mean with narrow variance
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Ten simultaneous cross-method comparisons are made without adjusting for the number of comparisons↳ Could also: A Friedman test followed by post-hoc Nemenyi or Holm-corrected Wilcoxon pairwise tests would also control the family-wise error rate across the full comparison set — Controlling for multiple simultaneous comparisons reduces the probability that at least one apparent superiority claim arises by chance when ten methods are evaluated together
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Feature selection uses RFECV; the stability of the selected gene set across cross-validation folds is not reported↳ Could also: Stability selection (Meinshausen & Bühlmann, 2010) or bootstrap-based selection frequency scores could also quantify how consistently each gene is chosen across resampling iterations — Selection-frequency metrics complement the dimensionality-reduction result by indicating whether the ∼127 selected genes form a reproducible signature or vary substantially across folds
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The nesting structure of cross-validation (outer evaluation loop vs. inner hyperparameter/feature-selection loop) is not specified in the available methodology text↳ Could also: Nested cross-validation — with a separate inner loop for feature selection and hyperparameter tuning and an outer loop for unbiased performance estimation — is also a standard approach when selection and tuning are coupled — Explicitly nested CV prevents optimistic bias that can arise when the same fold structure is used for both model selection and performance reporting
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Model calibration is addressed via isotonic regression with ECE minimisation, but no calibration visualisation (e.g., reliability diagrams) or per-dataset calibration scores are mentioned in the available text↳ Could also: Reliability diagrams paired with the Brier score or Expected Calibration Error reported per dataset could also illustrate and quantify calibration quality — Visual calibration curves identify probability regions where the model is systematically over- or under-confident, which is directly relevant for clinical threshold-based decision support
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
465 downstream papers · 6 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.
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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-41925147 (MICRO-AI)
Paper: Othman MA. "Machine learning-based DNA microarray analysis for disease detection using the MICRO-AI framework." Sci Prog 2026. PMID 41925147 / PMC13051084 / DOI 10.1177/00368504261436834.
Code: https://github.com/Manal-oth/MICRO-AI (commit ad27cab01ab83695980b7bed62459ef325508d7a,
pushed 2026-02-21). One file micro_ai.py (1080 lines) — a self-contained
scikit-learn-style framework class MICROAI (quantile-norm → ComBat → KNN-impute →
attention feature selection → RFECV → adaptive ensemble of GB/RF/SVM with isotonic
calibration). README is one sentence. NO data loader and NO driver that reproduces
the paper's tables: the __main__ block only runs the class on synthetic random
data (np.random.randn). License: none declared.
Data: GEO GSE2034 (Wang et al. 2005) — 286 lymph-node-negative primary breast
cancer patients, Affymetrix HG-U133A (GPL96), 22,283 probes. Binary label
relapse (1=True): 106 relapse (distant metastasis) vs 180 relapse-free. Publicly
downloadable series matrix. This is the dataset the RU brief points to.
In scope (pipeline-derived → attempted)
The paper evaluates MICRO-AI on six datasets (Table 5). We reproduce the GSE2034
row — the brief's named dataset — by running the authors' own micro_ai.py
on the real GSE2034 data with the described protocol (5-fold stratified CV).
| id | reported (Table) | value |
|---|---|---|
| C1 | dataset dims (Table 2) | GSE2034 = 286 samples × 22,283 probes, U133A, 2 classes |
| C2 | Accuracy (Table 5) | 95.8% |
| C3 | AUC (Table 5) | 0.978 |
| C4 | F1 (Table 5) | 95.5% |
| C5 | MCC (Table 5) | 0.914 |
| C6 | Sensitivity/Specificity (Table 5) | 94.2% / 97.1% |
| C7 | # selected genes (Table 6) | 127 genes (99.4% reduction) |
Protocol (Experimental Setup): "5-fold stratified cross-validation with inner 3-fold CV for hyperparameter tuning. Performance averaged over 10 random seeds."
Out of scope (not attempted, with reason)
- Other 5 datasets (GSE7390, TCGA-BRCA, GSE62254, E-MTAB-365, GSE9891): 80/20 — one clean, fully-specified dataset (GSE2034) is sufficient for a 1:1 honesty check; the framework and protocol are identical across rows, so GSE2034 is representative.
- Table 7 comparison methods (SVM-RFE, XGBoost, GeneFormer, scBERT, …): these are external baselines, not part of the shipped MICRO-AI code; reproducing 10 other tools is out of scope and not the paper's own contribution.
- Training-time column (52.3 s): hardware-dependent, not a scientific result.
Reproduction note / fabrication watch
GSE2034 distant-metastasis prediction is a notoriously hard prognosis task: the published literature (incl. the 76-gene Wang signature and many follow-ups) reports AUCs ≈ 0.6–0.7 and accuracies ≈ 0.65–0.70 on this exact cohort. A reported 95.8% accuracy / 0.978 AUC is far above anything in the field and is the kind of value that warrants a careful 1:1 check. We run the authors' own code on the authors' own data with an honest, leakage-free outer CV and report whatever it gives.
Assessments & scoring basis
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