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integrity","status":"pass","headline":"12 rows removed during cleaning (0.3%)","detail":"12 duplicate pairs (0 reversed), 0 self-pairs and 0 pairs with conflicting labels were removed; 3,600 canonical pairs remain.","ml_implication":"Reversed duplicates (A\u2013B and B\u2013A) placed on both sides of a random split leak labels directly. Train on the canonical pair list, not the raw file.","caveat":"","evidence":{"removed":12,"fraction":0.0033222591362126247},"chapter":"audit"},{"id":"negative_bias","title":"Degree bias in supplied negatives","status":"fail","headline":"Endpoint degree SMD 0.99 between positives and supplied negatives","detail":"Positive pairs join proteins with mean positive-network degree 16.12, supplied negatives 2.21. Degree-matched N2 brings the SMD to 0.13.","ml_implication":"A model can tell the classes apart by how well-studied the proteins are, without learning anything about interaction. Compare against degree-matched negatives before trusting a score.","caveat":"Constructed negatives are putative (unobserved) pairs, not experimentally established non-interactions.","evidence":{"smd":0.9908212550587586,"smd_n2":0.13390725179637364},"chapter":"negatives"},{"id":"degree_shortcut","title":"Degree-only shortcut","status":"fail","headline":"Degree alone reaches ROC-AUC 0.981 on the reference labels","detail":"A logistic model on four endpoint-degree summaries, with no sequence information, scores 0.981 on N0 and 0.511 on degree-matched N2 negatives.","ml_implication":"Most of the benchmark's label separation is reproducible from network degree. Headline scores on these labels mix interaction signal with annotation density.","caveat":"Degree is computed from the same incomplete positive graph that supplies the labels; it is a retrospective audit feature, not an input available for a protein with no known interactions.","evidence":{"degree_auc_n0":0.9811304012345679,"degree_auc_n2":0.5105825617283952},"chapter":"topology"},{"id":"length_shortcut","title":"Length-only shortcut","status":"pass","headline":"Sequence length alone: ROC-AUC 0.553","detail":"Endpoint sequence-length summaries used as the only features.","ml_implication":"Sequence length does not separate the labels.","caveat":"","evidence":{"length_auc_n0":0.5528472222222223},"chapter":"topology"},{"id":"novelty_drop","title":"Performance on unseen proteins","status":"fail","headline":"ROC-AUC falls by 0.193 from seen (R1) to unseen proteins (R3)","detail":"AAC+CTD MLP with the training set held fixed: R0 0.898, R1 0.906, R3 0.713.","ml_implication":"Random pair splits overstate performance on new proteins. Report the protein-disjoint R3 split if the model is meant for proteins without known interactions.","caveat":"R3 test sets are small; see the uncertainty chapter.","evidence":{"r0":0.8979166666666666,"r1":0.9059281842818427,"r3":0.7131410256410257,"drop":0.19278715864081708},"chapter":"novelty"},{"id":"matched_signal","title":"Signal after degree matching","status":"warn","headline":"Modest signal (0.712) with degree-matched negatives on unseen proteins","detail":"Best representation AAC+CTD, MLP trained and tested with N2 negatives at R3: ROC-AUC 0.712 (N0: 0.713). Component bootstrap 95% CI 0.000\u20130.717; above chance under both corrected tests: no.","ml_implication":"Some sequence signal remains, but it is small or uncertain; treat performance claims cautiously.","caveat":"A non-significant test does not establish equivalence to chance; few connected components limit what can be concluded.","evidence":{"representation":"AAC+CTD","auc_n2":0.7117159763313611,"auc_n0":0.7131410256410257,"significant":false},"chapter":"revalidation"},{"id":"independent_units","title":"Independent test units","status":"fail","headline":"R3 test graphs contain 2\u20137 connected components","detail":"Test pairs that share proteins are not independent. Uncertainty is computed by resampling whole connected components of the test-pair graph.","ml_implication":"Confidence intervals are wide and tests have little power; differences between models of a few hundredths are not interpretable.","caveat":"A non-significant test does not establish equivalence to chance; few connected components limit what can be concluded.","evidence":{"min_components":2,"max_components":7},"chapter":"uncertainty"}],"summary":{"verdict":"Needs controls","headline":"At least one artefact materially affects what models trained here will learn.","score":35,"n_fail":4,"n_warn":1,"n_pass":2,"score_note":"Heuristic summary of the diagnostics below (100 \u2212 15 per concern \u2212 5 per caution); not a statistical quantity."},"recommendations":[{"title":"Train on the canonical pair list","file":"canonical_pairs.tsv","text":"Duplicates and reversed duplicates have been removed; use this file instead of the raw input."},{"title":"Use degree-matched negatives","file":"negatives/N2.tsv","text":"Train and evaluate with the same degree-matched definition (N2, or N5 to also match length) and report the degree-only baseline."},{"title":"Report a protein-disjoint split","file":"splits/R3/","text":"R1/R2/R3 share one training set; R3 test pairs contain only proteins never seen in training. Report R3 for claims about new proteins."},{"title":"Report component-aware uncertainty","file":"tables/component_aware_uncertainty.csv","text":"Quote bootstrap and jackknife intervals over connected components, and add an independent benchmark where possible."},{"title":"Report both controls","file":"results.json","text":"Protein reuse and negative construction are distinct problems: a protein-disjoint split can keep biased negatives, and balanced negatives can still let proteins recur."}],"methods_text":"The dataset (600 proteins, 3,612 pairs) was audited and canonicalized to 3,600 unique undirected pairs (1,800 positive, 1,800 negative). Following the protein-novelty and negative-sampling control framework, a random pair split (R0) and a matched protein-novelty curve (R1 both proteins seen, R2 one unseen, R3 both unseen; shared training set of 1,945 pairs) were built from one partition of identical-sequence groups, with automated leakage gates. Putative negative sets (N0, N1, N2, N3, N5, N2-exact, N2-nearest) were constructed by one-sided substitution matched on positive-network degree and/or sequence length deciles. AAC+CTD descriptors (167-d) with symmetric pair fusion were used with lr, rf, mlp models. Degree-only and length-only classifiers were trained on the reference negatives and evaluated on each alternative set, and an MLP was re-evaluated with each negative definition used consistently for training and testing at R0 and R3. Uncertainty was quantified with a connected-component bootstrap (1000 replicates) and a delete-one-component jackknife, with Benjamini\u2013Hochberg adjustment. Seed 42.","thresholds":{"smd_warn":0.25,"smd_fail":0.8,"degree_auc_warn":0.65,"degree_auc_fail":0.75,"length_auc_warn":0.65,"novelty_drop_warn":0.03,"novelty_drop_fail":0.1,"chance_band":0.05,"modest_band":0.1,"components_warn":30,"components_fail":10,"species_auc_warn":0.6,"species_auc_fail":0.7,"cross_species_warn":0.1,"positive_cross_max":0.05}}