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integrity","status":"pass","headline":"24 rows removed during cleaning (0.2%)","detail":"24 duplicate pairs (0 reversed), 0 self-pairs and 0 pairs with conflicting labels were removed; 11,164 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":24,"fraction":0.0021451555237754737},"chapter":"audit"},{"id":"negative_bias","title":"Degree bias in supplied negatives","status":"fail","headline":"Endpoint degree SMD 1.16 between positives and supplied negatives","detail":"Positive pairs join proteins with mean positive-network degree 6.72, supplied negatives 3.34. Degree-matched N2 brings the SMD to 0.01.","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":1.1603894742223229,"smd_n2":0.010708048758428958},"chapter":"negatives"},{"id":"degree_shortcut","title":"Degree-only shortcut","status":"fail","headline":"Degree alone reaches ROC-AUC 0.962 on the reference labels","detail":"A logistic model on four endpoint-degree summaries, with no sequence information, scores 0.962 on N0 and 0.502 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":0.9624269349172752,"degree_auc_n2":0.5024849942573829},"chapter":"topology"},{"id":"length_shortcut","title":"Length-only shortcut","status":"pass","headline":"Sequence length alone: ROC-AUC 0.540","detail":"Endpoint sequence-length summaries used as the only features.","ml_implication":"Sequence length does not separate the labels.","caveat":"","evidence":{"length_auc_n0":0.5403425853718355},"chapter":"topology"},{"id":"novelty_drop","title":"Performance on unseen proteins","status":"fail","headline":"ROC-AUC falls by 0.109 from seen (R1) to unseen proteins (R3)","detail":"AAC+CTD MLP with the training set held fixed: R0 0.701, R1 0.697, R3 0.587.","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":0.7010329126596779,"r1":0.6966137420219053,"r3":0.5874064206240501,"drop":0.10920732139785516},"chapter":"novelty"},{"id":"matched_signal","title":"Signal after degree matching","status":"fail","headline":"Close to chance (0.517) with degree-matched negatives on unseen proteins","detail":"Best representation AAC+CTD, MLP trained and tested with N2 negatives at R3: ROC-AUC 0.517 (N0: 0.587). Component bootstrap 95% CI 0.400\u20130.960; above chance under both corrected tests: no.","ml_implication":"With degree shortcuts removed and proteins unseen, there is little sequence signal for a model to learn from this dataset as constructed.","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":0.517263568541495,"auc_n0":0.5874064206240501,"significant":false},"chapter":"revalidation"},{"id":"independent_units","title":"Independent test units","status":"fail","headline":"R3 test graphs contain 5\u201319 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":5,"max_components":19},"chapter":"uncertainty"}],"summary":{"verdict":"Shortcut-dominated","headline":"Most of this benchmark's apparent signal is reproduced by network degree, and little survives when that shortcut is removed.","score":25,"n_fail":5,"n_warn":0,"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 (2,497 proteins, 11,188 pairs) was audited and canonicalized to 11,164 unique undirected pairs (5,581 positive, 5,583 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 5,703 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}}