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0 within-species positives (< 50)"}]},"diagnostics":[{"id":"integrity","title":"Data integrity","status":"warn","headline":"1,815 rows removed during cleaning (2.8%)","detail":"536 duplicate pairs (4 reversed), 1,279 self-pairs and 0 pairs with conflicting labels were removed; 64,103 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":1815,"fraction":0.027534209168967504},"chapter":"audit"},{"id":"negative_bias","title":"Degree bias in supplied negatives","status":"fail","headline":"Endpoint degree SMD 0.83 between positives and supplied negatives","detail":"Positive pairs join proteins with mean positive-network degree 25.26, supplied negatives 6.77. 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":0.8271581112191706,"smd_n2":0.13013270954053732},"chapter":"negatives"},{"id":"degree_shortcut","title":"Degree-only shortcut","status":"fail","headline":"Degree alone reaches ROC-AUC 0.849 on the reference labels","detail":"A logistic model on four endpoint-degree summaries, with no sequence information, scores 0.849 on N0 and 0.528 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":0.8491888940484256,"degree_auc_n2":0.5281479369133252},"chapter":"topology"},{"id":"length_shortcut","title":"Length-only shortcut","status":"pass","headline":"Sequence length alone: ROC-AUC 0.517","detail":"Endpoint sequence-length summaries used as the only features.","ml_implication":"Sequence length does not separate the labels.","caveat":"","evidence":{"length_auc_n0":0.516769772221054},"chapter":"topology"},{"id":"novelty_drop","title":"Performance on unseen proteins","status":"fail","headline":"ROC-AUC falls by 0.260 from seen (R1) to unseen proteins (R3)","detail":"AAC+CTD MLP with the training set held fixed: R0 0.962, R1 0.962, R3 0.702.","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":0.9617568076680452,"r1":0.9622638001619294,"r3":0.7018410035345198,"drop":0.2604227966274095},"chapter":"novelty"},{"id":"matched_signal","title":"Signal after degree matching","status":"warn","headline":"Modest signal (0.584) with degree-matched negatives on unseen proteins","detail":"Best representation AAC+CTD, MLP trained and tested with N2 negatives at R3: ROC-AUC 0.584 (N0: 0.702). Component bootstrap 95% CI 0.472\u20130.636; 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":0.5838720032784646,"auc_n0":0.7018410035345198,"significant":false},"chapter":"revalidation"},{"id":"independent_units","title":"Independent test units","status":"warn","headline":"R3 test graphs contain 17\u201366 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":17,"max_components":66},"chapter":"uncertainty"},{"id":"taxonomic_shortcut","title":"Taxonomic shortcut","status":"fail","headline":"'Same species?' alone scores ROC-AUC 0.728 on pooled degree-matched negatives","detail":"Scoring a pair 1 when both proteins come from the same organism gives 0.728 on N2 and 0.500 on species-matched N2-species negatives.","ml_implication":"Pooled negatives that join organisms can be recognised from taxonomy alone; pooled performance partly measures species identification. Use species-matched negatives (N2-species) or evaluate within species.","caveat":"Constructed negatives are putative (unobserved) pairs, not experimentally established non-interactions.","evidence":{"same_species_auc_n2":0.7284066460325388,"same_species_auc_n2_species":0.5000001122533457},"chapter":"species"},{"id":"cross_species","title":"Cross-species negatives","status":"warn","headline":"45.3% of pooled N2 negatives join two species (positives: 0.2%)","detail":"Fraction of pairs whose endpoints are assigned to different organisms, per negative set.","ml_implication":"Negatives and positives differ in species composition, which a model can exploit.","caveat":"","evidence":{"positive_cross":0.0019267214150347441,"n2_cross":0.45336239300041065},"chapter":"species"},{"id":"within_species","title":"Within-species signal","status":"info","headline":"Within-species AAC+CTD MLP with N2 negatives: dmelanogaster 0.533, celegans 0.535, ecoli 0.589","detail":"Degree statistics and negatives are rebuilt inside each organism; positives and negatives are split 80/20 at random (proteins may recur, as in the source protocol).","ml_implication":"Within-species estimates remove the taxonomic shortcut but not protein reuse; compare with the pooled R3 result.","caveat":"","evidence":{"rows":[{"species":"dmelanogaster","negative_set":"N2","representation":"AAC+CTD","model":"mlp","train_pairs":34786,"test_pairs":8698,"roc_auc":0.5328243117287741,"pr_auc":0.5278976238659463,"mcc":0.039716826888256396,"f1":0.4966863477527413,"balanced_accuracy":0.5197746608415728,"accuracy":0.5197746608415728,"precision":0.5217721518987342,"recall":0.4739020464474592,"specificity":0.5656472752356864,"brier":0.3689543120417183,"ece_10bin":0.3188380967410451,"positive_prevalence":0.5,"n_test":8698},{"species":"celegans","negative_set":"N2","representation":"AAC+CTD","model":"mlp","train_pairs":6320,"test_pairs":1580,"roc_auc":0.5346306681621535,"pr_auc":0.5499270322313135,"mcc":0.04222189912964812,"f1":0.4832764505119454,"balanced_accuracy":0.5208860759493671,"accuracy":0.5208860759493671,"precision":0.5244444444444445,"recall":0.4481012658227848,"specificity":0.5936708860759494,"brier":0.3032280369519111,"ece_10bin":0.20545817576731387,"positive_prevalence":0.5,"n_test":1580},{"species":"ecoli","negative_set":"N2","representation":"AAC+CTD","model":"mlp","train_pairs":9450,"test_pairs":2364,"roc_auc":0.5889040629178226,"pr_auc":0.5651126450900485,"mcc":0.1455763128550736,"f1":0.5665236051502146,"balanced_accuracy":0.5727580372250423,"accuracy":0.5727580372250423,"precision":0.5749128919860628,"recall":0.5583756345177665,"specificity":0.5871404399323181,"brier":0.29444849875534285,"ece_10bin":0.1915360772407526,"positive_prevalence":0.5,"n_test":2364}]},"chapter":"species"}],"summary":{"verdict":"Needs controls","headline":"At least one artefact materially affects what models trained here will learn.","score":20,"n_fail":4,"n_warn":4,"n_pass":1,"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":"Match negatives within species","file":"negatives/N2-species.tsv","text":"Species-matched degree negatives remove the 'same organism' shortcut; also report within-species results."},{"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 (11,529 proteins, 65,918 pairs) was audited and canonicalized to 64,103 unique undirected pairs (31,678 positive, 32,425 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 31,863 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, N2-species) 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. Species diagnostics measured the cross-species fraction of each negative set, the ROC-AUC of a same-species indicator, species-matched degree negatives (N2-species), and within-species replication of the degree-only and sequence controls.","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}}