Dataset

OGBl-ppa PPI graph (embedding study)

Evidence concern: excluded from comparisons

The text's summary does not match the tables: it lists ProtT5 among structure embeddings while the tables print ProstT5; it reports a GraphSAGE with DistMult result (+38% Hits@100 on HuRI) although DistMult is not a column; and the stated gains (for example ESM-2 with GraphSAGE +6% Hits@100 on OGBl-ppa) cannot be reproduced from the printed table values (GS random 0.0104, ESM-2 0.2421). Values are kept as printed; comparisons should rely on the tables only after a reviewer has judged this. Random-feature baselines on OGBl-ppa (GCN 0.0588, GS 0.0104 Hits@100) look far below the public OGB leaderboard values the authors say they reproduced (Supplementary Table S2, not retrieved; reviewer did not verify the leaderboard values).The role of protein embeddings for protein-protein interaction prediction with graph neural networks · Results and discussion, first paragraph; Tables 5 and 6

Open Graph Benchmark ogbl-ppa protein association graph (576,289 nodes, 30,326,273 edges; STRING-derived, multi-species).

Evaluation results

154 evaluations · 154 results. Different protocols are not a single leaderboard.

Filter evaluations

Tested model or methodProtocolDatasetEvaluation setupMetricEvidence origin

Applied filters: All linked evaluations

Exact evaluated configurations and original reported results
Tested configuration	Protocol and dataset	Finding	Evidence and details
Configuration: GCN with ComplEx protein embeddings (function (knowledge-graph embedding)) (PPI embeddings study)	Protocol: OGBl-ppa PPI link prediction, Accuracy (Table 6) Dataset: OGBl-ppa PPI graph (embedding study)	0.984 Accuracy (OGBl-ppa PPI link prediction) fraction · higher Uncertainty: Not reported by the source: Only a table-wide maximum SD is printed (Table 5/6 footnote) Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons The text's summary does not match the tables: it lists ProtT5 among structure embeddings while the tables print ProstT5; it reports a GraphSAGE with DistMult result (+38% Hits@100 on HuRI) although DistMult is not a column; and the stated gains (for example ESM-2 with GraphSAGE +6% Hits@100 on OGBl-ppa) cannot be reproduced from the printed table values (GS random 0.0104, ESM-2 0.2421). Values are kept as printed; comparisons should rely on the tables only after a reviewer has judged this. Random-feature baselines on OGBl-ppa (GCN 0.0588, GS 0.0104 Hits@100) look far below the public OGB leaderboard values the authors say they reproduced (Supplementary Table S2, not retrieved; reviewer did not verify the leaderboard values).The role of protein embeddings for protein-protein interaction prediction with graph neural networks · Results and discussion, first paragraph; Tables 5 and 6 Methods, coverage and source GCN / ComplEx on OGBl-ppa (Accuracy) lit-interactions-20261011-ppiemb-protocol-ogbl-ppa-accuracy Aggregation: Not reported The role of protein embeddings for protein-protein interaction prediction with graph neural networks · Table 6, block 'OGBl-ppa', row 'GCN', column 'ComplEx'
Configuration: GCN with ComplEx protein embeddings (function (knowledge-graph embedding)) (PPI embeddings study)	Protocol: OGBl-ppa PPI link prediction, Hits@100 (Table 5) Dataset: OGBl-ppa PPI graph (embedding study)	0.245 Hits at 100 (OGBl-ppa PPI link prediction) fraction · higher Uncertainty: Not reported by the source: Only a table-wide maximum SD is printed (Table 5/6 footnote) Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons The text's summary does not match the tables: it lists ProtT5 among structure embeddings while the tables print ProstT5; it reports a GraphSAGE with DistMult result (+38% Hits@100 on HuRI) although DistMult is not a column; and the stated gains (for example ESM-2 with GraphSAGE +6% Hits@100 on OGBl-ppa) cannot be reproduced from the printed table values (GS random 0.0104, ESM-2 0.2421). Values are kept as printed; comparisons should rely on the tables only after a reviewer has judged this. Random-feature baselines on OGBl-ppa (GCN 0.0588, GS 0.0104 Hits@100) look far below the public OGB leaderboard values the authors say they reproduced (Supplementary Table S2, not retrieved; reviewer did not verify the leaderboard values).The role of protein embeddings for protein-protein interaction prediction with graph neural networks · Results and discussion, first paragraph; Tables 5 and 6 Methods, coverage and source GCN / ComplEx on OGBl-ppa (Hits@100) lit-interactions-20261011-ppiemb-protocol-ogbl-ppa-hits-100 Aggregation: Not reported The role of protein embeddings for protein-protein interaction prediction with graph neural networks · Table 5, block 'OGBl-ppa', row 'GCN', column 'ComplEx'
Configuration: GCN with majority vote across the representations (PPI embeddings study)	Protocol: OGBl-ppa PPI link prediction, Accuracy (Table 6) Dataset: OGBl-ppa PPI graph (embedding study)	0.942 Accuracy (OGBl-ppa PPI link prediction) fraction · higher Uncertainty: Not reported by the source: Only a table-wide maximum SD is printed (Table 5/6 footnote) Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Evidence concern: excluded from comparisons The text's summary does not match the tables: it lists ProtT5 among structure embeddings while the tables print ProstT5; it reports a GraphSAGE with DistMult result (+38% Hits@100 on HuRI) although DistMult is not a column; and the stated gains (for example ESM-2 with GraphSAGE +6% Hits@100 on OGBl-ppa) cannot be reproduced from the printed table values (GS random 0.0104, ESM-2 0.2421). Values are kept as printed; comparisons should rely on the tables only after a reviewer has judged this. Random-feature baselines on OGBl-ppa (GCN 0.0588, GS 0.0104 Hits@100) look far below the public OGB leaderboard values the authors say they reproduced (Supplementary Table S2, not retrieved; reviewer did not verify the leaderboard values).The role of protein embeddings for protein-protein interaction prediction with graph neural networks · Results and discussion, first paragraph; Tables 5 and 6 Methods, coverage and source GCN / Ensemble on OGBl-ppa (Accuracy) lit-interactions-20261011-ppiemb-protocol-ogbl-ppa-accuracy Aggregation: Not reported The role of protein embeddings for protein-protein interaction prediction with graph neural networks · Table 6, block 'OGBl-ppa', row 'GCN', column 'Ensemble'
Configuration: GCN with majority vote across the representations (PPI embeddings study)	Protocol: OGBl-ppa PPI link prediction, Hits@100 (Table 5) Dataset: OGBl-ppa PPI graph (embedding study)	0.21 Hits at 100 (OGBl-ppa PPI link prediction) fraction · higher Uncertainty: Not reported by the source: Only a table-wide maximum SD is printed (Table 5/6 footnote) Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Evidence concern: excluded from comparisons The text's summary does not match the tables: it lists ProtT5 among structure embeddings while the tables print ProstT5; it reports a GraphSAGE with DistMult result (+38% Hits@100 on HuRI) although DistMult is not a column; and the stated gains (for example ESM-2 with GraphSAGE +6% Hits@100 on OGBl-ppa) cannot be reproduced from the printed table values (GS random 0.0104, ESM-2 0.2421). Values are kept as printed; comparisons should rely on the tables only after a reviewer has judged this. Random-feature baselines on OGBl-ppa (GCN 0.0588, GS 0.0104 Hits@100) look far below the public OGB leaderboard values the authors say they reproduced (Supplementary Table S2, not retrieved; reviewer did not verify the leaderboard values).The role of protein embeddings for protein-protein interaction prediction with graph neural networks · Results and discussion, first paragraph; Tables 5 and 6 Methods, coverage and source GCN / Ensemble on OGBl-ppa (Hits@100) lit-interactions-20261011-ppiemb-protocol-ogbl-ppa-hits-100 Aggregation: Not reported The role of protein embeddings for protein-protein interaction prediction with graph neural networks · Table 5, block 'OGBl-ppa', row 'GCN', column 'Ensemble'
Configuration: GCN with ESM-2 protein embeddings (sequence) (PPI embeddings study)	Protocol: OGBl-ppa PPI link prediction, Accuracy (Table 6) Dataset: OGBl-ppa PPI graph (embedding study)	0.873 Accuracy (OGBl-ppa PPI link prediction) fraction · higher Uncertainty: Not reported by the source: Only a table-wide maximum SD is printed (Table 5/6 footnote) Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons The text's summary does not match the tables: it lists ProtT5 among structure embeddings while the tables print ProstT5; it reports a GraphSAGE with DistMult result (+38% Hits@100 on HuRI) although DistMult is not a column; and the stated gains (for example ESM-2 with GraphSAGE +6% Hits@100 on OGBl-ppa) cannot be reproduced from the printed table values (GS random 0.0104, ESM-2 0.2421). Values are kept as printed; comparisons should rely on the tables only after a reviewer has judged this. Random-feature baselines on OGBl-ppa (GCN 0.0588, GS 0.0104 Hits@100) look far below the public OGB leaderboard values the authors say they reproduced (Supplementary Table S2, not retrieved; reviewer did not verify the leaderboard values).The role of protein embeddings for protein-protein interaction prediction with graph neural networks · Results and discussion, first paragraph; Tables 5 and 6 Methods, coverage and source GCN / ESM-2 on OGBl-ppa (Accuracy) lit-interactions-20261011-ppiemb-protocol-ogbl-ppa-accuracy Aggregation: Not reported The role of protein embeddings for protein-protein interaction prediction with graph neural networks · Table 6, block 'OGBl-ppa', row 'GCN', column 'ESM-2'
Configuration: GCN with ESM-2 protein embeddings (sequence) (PPI embeddings study)	Protocol: OGBl-ppa PPI link prediction, Hits@100 (Table 5) Dataset: OGBl-ppa PPI graph (embedding study)	0.16 Hits at 100 (OGBl-ppa PPI link prediction) fraction · higher Uncertainty: Not reported by the source: Only a table-wide maximum SD is printed (Table 5/6 footnote) Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons The text's summary does not match the tables: it lists ProtT5 among structure embeddings while the tables print ProstT5; it reports a GraphSAGE with DistMult result (+38% Hits@100 on HuRI) although DistMult is not a column; and the stated gains (for example ESM-2 with GraphSAGE +6% Hits@100 on OGBl-ppa) cannot be reproduced from the printed table values (GS random 0.0104, ESM-2 0.2421). Values are kept as printed; comparisons should rely on the tables only after a reviewer has judged this. Random-feature baselines on OGBl-ppa (GCN 0.0588, GS 0.0104 Hits@100) look far below the public OGB leaderboard values the authors say they reproduced (Supplementary Table S2, not retrieved; reviewer did not verify the leaderboard values).The role of protein embeddings for protein-protein interaction prediction with graph neural networks · Results and discussion, first paragraph; Tables 5 and 6 Methods, coverage and source GCN / ESM-2 on OGBl-ppa (Hits@100) lit-interactions-20261011-ppiemb-protocol-ogbl-ppa-hits-100 Aggregation: Not reported The role of protein embeddings for protein-protein interaction prediction with graph neural networks · Table 5, block 'OGBl-ppa', row 'GCN', column 'ESM-2'
Configuration: GCN with ESM-IF1 protein embeddings (structure) (PPI embeddings study)	Protocol: OGBl-ppa PPI link prediction, Accuracy (Table 6) Dataset: OGBl-ppa PPI graph (embedding study)	0.981 Accuracy (OGBl-ppa PPI link prediction) fraction · higher Uncertainty: Not reported by the source: Only a table-wide maximum SD is printed (Table 5/6 footnote) Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons The text's summary does not match the tables: it lists ProtT5 among structure embeddings while the tables print ProstT5; it reports a GraphSAGE with DistMult result (+38% Hits@100 on HuRI) although DistMult is not a column; and the stated gains (for example ESM-2 with GraphSAGE +6% Hits@100 on OGBl-ppa) cannot be reproduced from the printed table values (GS random 0.0104, ESM-2 0.2421). Values are kept as printed; comparisons should rely on the tables only after a reviewer has judged this. Random-feature baselines on OGBl-ppa (GCN 0.0588, GS 0.0104 Hits@100) look far below the public OGB leaderboard values the authors say they reproduced (Supplementary Table S2, not retrieved; reviewer did not verify the leaderboard values).The role of protein embeddings for protein-protein interaction prediction with graph neural networks · Results and discussion, first paragraph; Tables 5 and 6 Methods, coverage and source GCN / ESM-IF1 on OGBl-ppa (Accuracy) lit-interactions-20261011-ppiemb-protocol-ogbl-ppa-accuracy Aggregation: Not reported The role of protein embeddings for protein-protein interaction prediction with graph neural networks · Table 6, block 'OGBl-ppa', row 'GCN', column 'ESM-IF1'
Configuration: GCN with ESM-IF1 protein embeddings (structure) (PPI embeddings study)	Protocol: OGBl-ppa PPI link prediction, Hits@100 (Table 5) Dataset: OGBl-ppa PPI graph (embedding study)	0.185 Hits at 100 (OGBl-ppa PPI link prediction) fraction · higher Uncertainty: Not reported by the source: Only a table-wide maximum SD is printed (Table 5/6 footnote) Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons The text's summary does not match the tables: it lists ProtT5 among structure embeddings while the tables print ProstT5; it reports a GraphSAGE with DistMult result (+38% Hits@100 on HuRI) although DistMult is not a column; and the stated gains (for example ESM-2 with GraphSAGE +6% Hits@100 on OGBl-ppa) cannot be reproduced from the printed table values (GS random 0.0104, ESM-2 0.2421). Values are kept as printed; comparisons should rely on the tables only after a reviewer has judged this. Random-feature baselines on OGBl-ppa (GCN 0.0588, GS 0.0104 Hits@100) look far below the public OGB leaderboard values the authors say they reproduced (Supplementary Table S2, not retrieved; reviewer did not verify the leaderboard values).The role of protein embeddings for protein-protein interaction prediction with graph neural networks · Results and discussion, first paragraph; Tables 5 and 6 Methods, coverage and source GCN / ESM-IF1 on OGBl-ppa (Hits@100) lit-interactions-20261011-ppiemb-protocol-ogbl-ppa-hits-100 Aggregation: Not reported The role of protein embeddings for protein-protein interaction prediction with graph neural networks · Table 5, block 'OGBl-ppa', row 'GCN', column 'ESM-IF1'
Configuration: GCN with PiFold protein embeddings (structure) (PPI embeddings study)	Protocol: OGBl-ppa PPI link prediction, Accuracy (Table 6) Dataset: OGBl-ppa PPI graph (embedding study)	0.899 Accuracy (OGBl-ppa PPI link prediction) fraction · higher Uncertainty: Not reported by the source: Only a table-wide maximum SD is printed (Table 5/6 footnote) Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons The text's summary does not match the tables: it lists ProtT5 among structure embeddings while the tables print ProstT5; it reports a GraphSAGE with DistMult result (+38% Hits@100 on HuRI) although DistMult is not a column; and the stated gains (for example ESM-2 with GraphSAGE +6% Hits@100 on OGBl-ppa) cannot be reproduced from the printed table values (GS random 0.0104, ESM-2 0.2421). Values are kept as printed; comparisons should rely on the tables only after a reviewer has judged this. Random-feature baselines on OGBl-ppa (GCN 0.0588, GS 0.0104 Hits@100) look far below the public OGB leaderboard values the authors say they reproduced (Supplementary Table S2, not retrieved; reviewer did not verify the leaderboard values).The role of protein embeddings for protein-protein interaction prediction with graph neural networks · Results and discussion, first paragraph; Tables 5 and 6 Methods, coverage and source GCN / PiFold on OGBl-ppa (Accuracy) lit-interactions-20261011-ppiemb-protocol-ogbl-ppa-accuracy Aggregation: Not reported The role of protein embeddings for protein-protein interaction prediction with graph neural networks · Table 6, block 'OGBl-ppa', row 'GCN', column 'PiFold'
Configuration: GCN with PiFold protein embeddings (structure) (PPI embeddings study)	Protocol: OGBl-ppa PPI link prediction, Hits@100 (Table 5) Dataset: OGBl-ppa PPI graph (embedding study)	0.189 Hits at 100 (OGBl-ppa PPI link prediction) fraction · higher Uncertainty: Not reported by the source: Only a table-wide maximum SD is printed (Table 5/6 footnote) Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons The text's summary does not match the tables: it lists ProtT5 among structure embeddings while the tables print ProstT5; it reports a GraphSAGE with DistMult result (+38% Hits@100 on HuRI) although DistMult is not a column; and the stated gains (for example ESM-2 with GraphSAGE +6% Hits@100 on OGBl-ppa) cannot be reproduced from the printed table values (GS random 0.0104, ESM-2 0.2421). Values are kept as printed; comparisons should rely on the tables only after a reviewer has judged this. Random-feature baselines on OGBl-ppa (GCN 0.0588, GS 0.0104 Hits@100) look far below the public OGB leaderboard values the authors say they reproduced (Supplementary Table S2, not retrieved; reviewer did not verify the leaderboard values).The role of protein embeddings for protein-protein interaction prediction with graph neural networks · Results and discussion, first paragraph; Tables 5 and 6 Methods, coverage and source GCN / PiFold on OGBl-ppa (Hits@100) lit-interactions-20261011-ppiemb-protocol-ogbl-ppa-hits-100 Aggregation: Not reported The role of protein embeddings for protein-protein interaction prediction with graph neural networks · Table 5, block 'OGBl-ppa', row 'GCN', column 'PiFold'
Configuration: GCN with ProGen2 protein embeddings (sequence) (PPI embeddings study)	Protocol: OGBl-ppa PPI link prediction, Accuracy (Table 6) Dataset: OGBl-ppa PPI graph (embedding study)	0.913 Accuracy (OGBl-ppa PPI link prediction) fraction · higher Uncertainty: Not reported by the source: Only a table-wide maximum SD is printed (Table 5/6 footnote) Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons The text's summary does not match the tables: it lists ProtT5 among structure embeddings while the tables print ProstT5; it reports a GraphSAGE with DistMult result (+38% Hits@100 on HuRI) although DistMult is not a column; and the stated gains (for example ESM-2 with GraphSAGE +6% Hits@100 on OGBl-ppa) cannot be reproduced from the printed table values (GS random 0.0104, ESM-2 0.2421). Values are kept as printed; comparisons should rely on the tables only after a reviewer has judged this. Random-feature baselines on OGBl-ppa (GCN 0.0588, GS 0.0104 Hits@100) look far below the public OGB leaderboard values the authors say they reproduced (Supplementary Table S2, not retrieved; reviewer did not verify the leaderboard values).The role of protein embeddings for protein-protein interaction prediction with graph neural networks · Results and discussion, first paragraph; Tables 5 and 6 Methods, coverage and source GCN / ProGen2 on OGBl-ppa (Accuracy) lit-interactions-20261011-ppiemb-protocol-ogbl-ppa-accuracy Aggregation: Not reported The role of protein embeddings for protein-protein interaction prediction with graph neural networks · Table 6, block 'OGBl-ppa', row 'GCN', column 'ProGen2'
Configuration: GCN with ProGen2 protein embeddings (sequence) (PPI embeddings study)	Protocol: OGBl-ppa PPI link prediction, Hits@100 (Table 5) Dataset: OGBl-ppa PPI graph (embedding study)	0.218 Hits at 100 (OGBl-ppa PPI link prediction) fraction · higher Uncertainty: Not reported by the source: Only a table-wide maximum SD is printed (Table 5/6 footnote) Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons The text's summary does not match the tables: it lists ProtT5 among structure embeddings while the tables print ProstT5; it reports a GraphSAGE with DistMult result (+38% Hits@100 on HuRI) although DistMult is not a column; and the stated gains (for example ESM-2 with GraphSAGE +6% Hits@100 on OGBl-ppa) cannot be reproduced from the printed table values (GS random 0.0104, ESM-2 0.2421). Values are kept as printed; comparisons should rely on the tables only after a reviewer has judged this. Random-feature baselines on OGBl-ppa (GCN 0.0588, GS 0.0104 Hits@100) look far below the public OGB leaderboard values the authors say they reproduced (Supplementary Table S2, not retrieved; reviewer did not verify the leaderboard values).The role of protein embeddings for protein-protein interaction prediction with graph neural networks · Results and discussion, first paragraph; Tables 5 and 6 Methods, coverage and source GCN / ProGen2 on OGBl-ppa (Hits@100) lit-interactions-20261011-ppiemb-protocol-ogbl-ppa-hits-100 Aggregation: Not reported The role of protein embeddings for protein-protein interaction prediction with graph neural networks · Table 5, block 'OGBl-ppa', row 'GCN', column 'ProGen2'
Configuration: GCN with ProstT5 protein embeddings (structure) (PPI embeddings study)	Protocol: OGBl-ppa PPI link prediction, Accuracy (Table 6) Dataset: OGBl-ppa PPI graph (embedding study)	0.808 Accuracy (OGBl-ppa PPI link prediction) fraction · higher Uncertainty: Not reported by the source: Only a table-wide maximum SD is printed (Table 5/6 footnote) Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons The text's summary does not match the tables: it lists ProtT5 among structure embeddings while the tables print ProstT5; it reports a GraphSAGE with DistMult result (+38% Hits@100 on HuRI) although DistMult is not a column; and the stated gains (for example ESM-2 with GraphSAGE +6% Hits@100 on OGBl-ppa) cannot be reproduced from the printed table values (GS random 0.0104, ESM-2 0.2421). Values are kept as printed; comparisons should rely on the tables only after a reviewer has judged this. Random-feature baselines on OGBl-ppa (GCN 0.0588, GS 0.0104 Hits@100) look far below the public OGB leaderboard values the authors say they reproduced (Supplementary Table S2, not retrieved; reviewer did not verify the leaderboard values).The role of protein embeddings for protein-protein interaction prediction with graph neural networks · Results and discussion, first paragraph; Tables 5 and 6 Methods, coverage and source GCN / ProstT5 on OGBl-ppa (Accuracy) lit-interactions-20261011-ppiemb-protocol-ogbl-ppa-accuracy Aggregation: Not reported The role of protein embeddings for protein-protein interaction prediction with graph neural networks · Table 6, block 'OGBl-ppa', row 'GCN', column 'ProstT5'
Configuration: GCN with ProstT5 protein embeddings (structure) (PPI embeddings study)	Protocol: OGBl-ppa PPI link prediction, Hits@100 (Table 5) Dataset: OGBl-ppa PPI graph (embedding study)	0.162 Hits at 100 (OGBl-ppa PPI link prediction) fraction · higher Uncertainty: Not reported by the source: Only a table-wide maximum SD is printed (Table 5/6 footnote) Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons The text's summary does not match the tables: it lists ProtT5 among structure embeddings while the tables print ProstT5; it reports a GraphSAGE with DistMult result (+38% Hits@100 on HuRI) although DistMult is not a column; and the stated gains (for example ESM-2 with GraphSAGE +6% Hits@100 on OGBl-ppa) cannot be reproduced from the printed table values (GS random 0.0104, ESM-2 0.2421). Values are kept as printed; comparisons should rely on the tables only after a reviewer has judged this. Random-feature baselines on OGBl-ppa (GCN 0.0588, GS 0.0104 Hits@100) look far below the public OGB leaderboard values the authors say they reproduced (Supplementary Table S2, not retrieved; reviewer did not verify the leaderboard values).The role of protein embeddings for protein-protein interaction prediction with graph neural networks · Results and discussion, first paragraph; Tables 5 and 6 Methods, coverage and source GCN / ProstT5 on OGBl-ppa (Hits@100) lit-interactions-20261011-ppiemb-protocol-ogbl-ppa-hits-100 Aggregation: Not reported The role of protein embeddings for protein-protein interaction prediction with graph neural networks · Table 5, block 'OGBl-ppa', row 'GCN', column 'ProstT5'
Configuration: GCN with ProtTrans protein embeddings (sequence) (PPI embeddings study)	Protocol: OGBl-ppa PPI link prediction, Accuracy (Table 6) Dataset: OGBl-ppa PPI graph (embedding study)	0.871 Accuracy (OGBl-ppa PPI link prediction) fraction · higher Uncertainty: Not reported by the source: Only a table-wide maximum SD is printed (Table 5/6 footnote) Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons The text's summary does not match the tables: it lists ProtT5 among structure embeddings while the tables print ProstT5; it reports a GraphSAGE with DistMult result (+38% Hits@100 on HuRI) although DistMult is not a column; and the stated gains (for example ESM-2 with GraphSAGE +6% Hits@100 on OGBl-ppa) cannot be reproduced from the printed table values (GS random 0.0104, ESM-2 0.2421). Values are kept as printed; comparisons should rely on the tables only after a reviewer has judged this. Random-feature baselines on OGBl-ppa (GCN 0.0588, GS 0.0104 Hits@100) look far below the public OGB leaderboard values the authors say they reproduced (Supplementary Table S2, not retrieved; reviewer did not verify the leaderboard values).The role of protein embeddings for protein-protein interaction prediction with graph neural networks · Results and discussion, first paragraph; Tables 5 and 6 Methods, coverage and source GCN / ProtTrans on OGBl-ppa (Accuracy) lit-interactions-20261011-ppiemb-protocol-ogbl-ppa-accuracy Aggregation: Not reported The role of protein embeddings for protein-protein interaction prediction with graph neural networks · Table 6, block 'OGBl-ppa', row 'GCN', column 'ProtTrans'
Configuration: GCN with ProtTrans protein embeddings (sequence) (PPI embeddings study)	Protocol: OGBl-ppa PPI link prediction, Hits@100 (Table 5) Dataset: OGBl-ppa PPI graph (embedding study)	0.168 Hits at 100 (OGBl-ppa PPI link prediction) fraction · higher Uncertainty: Not reported by the source: Only a table-wide maximum SD is printed (Table 5/6 footnote) Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons The text's summary does not match the tables: it lists ProtT5 among structure embeddings while the tables print ProstT5; it reports a GraphSAGE with DistMult result (+38% Hits@100 on HuRI) although DistMult is not a column; and the stated gains (for example ESM-2 with GraphSAGE +6% Hits@100 on OGBl-ppa) cannot be reproduced from the printed table values (GS random 0.0104, ESM-2 0.2421). Values are kept as printed; comparisons should rely on the tables only after a reviewer has judged this. Random-feature baselines on OGBl-ppa (GCN 0.0588, GS 0.0104 Hits@100) look far below the public OGB leaderboard values the authors say they reproduced (Supplementary Table S2, not retrieved; reviewer did not verify the leaderboard values).The role of protein embeddings for protein-protein interaction prediction with graph neural networks · Results and discussion, first paragraph; Tables 5 and 6 Methods, coverage and source GCN / ProtTrans on OGBl-ppa (Hits@100) lit-interactions-20261011-ppiemb-protocol-ogbl-ppa-hits-100 Aggregation: Not reported The role of protein embeddings for protein-protein interaction prediction with graph neural networks · Table 5, block 'OGBl-ppa', row 'GCN', column 'ProtTrans'
Configuration: GCN with randomly initialised node features (baseline) (PPI embeddings study)	Protocol: OGBl-ppa PPI link prediction, Accuracy (Table 6) Dataset: OGBl-ppa PPI graph (embedding study)	0.867 Accuracy (OGBl-ppa PPI link prediction) fraction · higher Uncertainty: Not reported by the source: Only a table-wide maximum SD is printed (Table 5/6 footnote) Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons The text's summary does not match the tables: it lists ProtT5 among structure embeddings while the tables print ProstT5; it reports a GraphSAGE with DistMult result (+38% Hits@100 on HuRI) although DistMult is not a column; and the stated gains (for example ESM-2 with GraphSAGE +6% Hits@100 on OGBl-ppa) cannot be reproduced from the printed table values (GS random 0.0104, ESM-2 0.2421). Values are kept as printed; comparisons should rely on the tables only after a reviewer has judged this. Random-feature baselines on OGBl-ppa (GCN 0.0588, GS 0.0104 Hits@100) look far below the public OGB leaderboard values the authors say they reproduced (Supplementary Table S2, not retrieved; reviewer did not verify the leaderboard values).The role of protein embeddings for protein-protein interaction prediction with graph neural networks · Results and discussion, first paragraph; Tables 5 and 6 Methods, coverage and source GCN / Random on OGBl-ppa (Accuracy) lit-interactions-20261011-ppiemb-protocol-ogbl-ppa-accuracy Aggregation: Not reported The role of protein embeddings for protein-protein interaction prediction with graph neural networks · Table 6, block 'OGBl-ppa', row 'GCN', column 'Random'
Configuration: GCN with randomly initialised node features (baseline) (PPI embeddings study)	Protocol: OGBl-ppa PPI link prediction, Hits@100 (Table 5) Dataset: OGBl-ppa PPI graph (embedding study)	0.0588 Hits at 100 (OGBl-ppa PPI link prediction) fraction · higher Uncertainty: Not reported by the source: Only a table-wide maximum SD is printed (Table 5/6 footnote) Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons The text's summary does not match the tables: it lists ProtT5 among structure embeddings while the tables print ProstT5; it reports a GraphSAGE with DistMult result (+38% Hits@100 on HuRI) although DistMult is not a column; and the stated gains (for example ESM-2 with GraphSAGE +6% Hits@100 on OGBl-ppa) cannot be reproduced from the printed table values (GS random 0.0104, ESM-2 0.2421). Values are kept as printed; comparisons should rely on the tables only after a reviewer has judged this. Random-feature baselines on OGBl-ppa (GCN 0.0588, GS 0.0104 Hits@100) look far below the public OGB leaderboard values the authors say they reproduced (Supplementary Table S2, not retrieved; reviewer did not verify the leaderboard values).The role of protein embeddings for protein-protein interaction prediction with graph neural networks · Results and discussion, first paragraph; Tables 5 and 6 Methods, coverage and source GCN / Random on OGBl-ppa (Hits@100) lit-interactions-20261011-ppiemb-protocol-ogbl-ppa-hits-100 Aggregation: Not reported The role of protein embeddings for protein-protein interaction prediction with graph neural networks · Table 5, block 'OGBl-ppa', row 'GCN', column 'Random'
Configuration: GCN with RDF2Vec protein embeddings (function (knowledge-graph embedding)) (PPI embeddings study)	Protocol: OGBl-ppa PPI link prediction, Accuracy (Table 6) Dataset: OGBl-ppa PPI graph (embedding study)	0.982 Accuracy (OGBl-ppa PPI link prediction) fraction · higher Uncertainty: Not reported by the source: Only a table-wide maximum SD is printed (Table 5/6 footnote) Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons The text's summary does not match the tables: it lists ProtT5 among structure embeddings while the tables print ProstT5; it reports a GraphSAGE with DistMult result (+38% Hits@100 on HuRI) although DistMult is not a column; and the stated gains (for example ESM-2 with GraphSAGE +6% Hits@100 on OGBl-ppa) cannot be reproduced from the printed table values (GS random 0.0104, ESM-2 0.2421). Values are kept as printed; comparisons should rely on the tables only after a reviewer has judged this. Random-feature baselines on OGBl-ppa (GCN 0.0588, GS 0.0104 Hits@100) look far below the public OGB leaderboard values the authors say they reproduced (Supplementary Table S2, not retrieved; reviewer did not verify the leaderboard values).The role of protein embeddings for protein-protein interaction prediction with graph neural networks · Results and discussion, first paragraph; Tables 5 and 6 Methods, coverage and source GCN / RDF2Vec on OGBl-ppa (Accuracy) lit-interactions-20261011-ppiemb-protocol-ogbl-ppa-accuracy Aggregation: Not reported The role of protein embeddings for protein-protein interaction prediction with graph neural networks · Table 6, block 'OGBl-ppa', row 'GCN', column 'RDF2Vec'
Configuration: GCN with RDF2Vec protein embeddings (function (knowledge-graph embedding)) (PPI embeddings study)	Protocol: OGBl-ppa PPI link prediction, Hits@100 (Table 5) Dataset: OGBl-ppa PPI graph (embedding study)	0.255 Hits at 100 (OGBl-ppa PPI link prediction) fraction · higher Uncertainty: Not reported by the source: Only a table-wide maximum SD is printed (Table 5/6 footnote) Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons The text's summary does not match the tables: it lists ProtT5 among structure embeddings while the tables print ProstT5; it reports a GraphSAGE with DistMult result (+38% Hits@100 on HuRI) although DistMult is not a column; and the stated gains (for example ESM-2 with GraphSAGE +6% Hits@100 on OGBl-ppa) cannot be reproduced from the printed table values (GS random 0.0104, ESM-2 0.2421). Values are kept as printed; comparisons should rely on the tables only after a reviewer has judged this. Random-feature baselines on OGBl-ppa (GCN 0.0588, GS 0.0104 Hits@100) look far below the public OGB leaderboard values the authors say they reproduced (Supplementary Table S2, not retrieved; reviewer did not verify the leaderboard values).The role of protein embeddings for protein-protein interaction prediction with graph neural networks · Results and discussion, first paragraph; Tables 5 and 6 Methods, coverage and source GCN / RDF2Vec on OGBl-ppa (Hits@100) lit-interactions-20261011-ppiemb-protocol-ogbl-ppa-hits-100 Aggregation: Not reported The role of protein embeddings for protein-protein interaction prediction with graph neural networks · Table 5, block 'OGBl-ppa', row 'GCN', column 'RDF2Vec'
Configuration: GCN with TransE protein embeddings (function (knowledge-graph embedding)) (PPI embeddings study)	Protocol: OGBl-ppa PPI link prediction, Accuracy (Table 6) Dataset: OGBl-ppa PPI graph (embedding study)	0.901 Accuracy (OGBl-ppa PPI link prediction) fraction · higher Uncertainty: Not reported by the source: Only a table-wide maximum SD is printed (Table 5/6 footnote) Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons The text's summary does not match the tables: it lists ProtT5 among structure embeddings while the tables print ProstT5; it reports a GraphSAGE with DistMult result (+38% Hits@100 on HuRI) although DistMult is not a column; and the stated gains (for example ESM-2 with GraphSAGE +6% Hits@100 on OGBl-ppa) cannot be reproduced from the printed table values (GS random 0.0104, ESM-2 0.2421). Values are kept as printed; comparisons should rely on the tables only after a reviewer has judged this. Random-feature baselines on OGBl-ppa (GCN 0.0588, GS 0.0104 Hits@100) look far below the public OGB leaderboard values the authors say they reproduced (Supplementary Table S2, not retrieved; reviewer did not verify the leaderboard values).The role of protein embeddings for protein-protein interaction prediction with graph neural networks · Results and discussion, first paragraph; Tables 5 and 6 Methods, coverage and source GCN / TransE on OGBl-ppa (Accuracy) lit-interactions-20261011-ppiemb-protocol-ogbl-ppa-accuracy Aggregation: Not reported The role of protein embeddings for protein-protein interaction prediction with graph neural networks · Table 6, block 'OGBl-ppa', row 'GCN', column 'TransE'
Configuration: GCN with TransE protein embeddings (function (knowledge-graph embedding)) (PPI embeddings study)	Protocol: OGBl-ppa PPI link prediction, Hits@100 (Table 5) Dataset: OGBl-ppa PPI graph (embedding study)	0.0382 Hits at 100 (OGBl-ppa PPI link prediction) fraction · higher Uncertainty: Not reported by the source: Only a table-wide maximum SD is printed (Table 5/6 footnote) Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons The text's summary does not match the tables: it lists ProtT5 among structure embeddings while the tables print ProstT5; it reports a GraphSAGE with DistMult result (+38% Hits@100 on HuRI) although DistMult is not a column; and the stated gains (for example ESM-2 with GraphSAGE +6% Hits@100 on OGBl-ppa) cannot be reproduced from the printed table values (GS random 0.0104, ESM-2 0.2421). Values are kept as printed; comparisons should rely on the tables only after a reviewer has judged this. Random-feature baselines on OGBl-ppa (GCN 0.0588, GS 0.0104 Hits@100) look far below the public OGB leaderboard values the authors say they reproduced (Supplementary Table S2, not retrieved; reviewer did not verify the leaderboard values).The role of protein embeddings for protein-protein interaction prediction with graph neural networks · Results and discussion, first paragraph; Tables 5 and 6 Methods, coverage and source GCN / TransE on OGBl-ppa (Hits@100) lit-interactions-20261011-ppiemb-protocol-ogbl-ppa-hits-100 Aggregation: Not reported The role of protein embeddings for protein-protein interaction prediction with graph neural networks · Table 5, block 'OGBl-ppa', row 'GCN', column 'TransE'
Configuration: GraphSAGE with ComplEx protein embeddings (function (knowledge-graph embedding)) (PPI embeddings study)	Protocol: OGBl-ppa PPI link prediction, Accuracy (Table 6) Dataset: OGBl-ppa PPI graph (embedding study)	0.974 Accuracy (OGBl-ppa PPI link prediction) fraction · higher Uncertainty: Not reported by the source: Only a table-wide maximum SD is printed (Table 5/6 footnote) Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons The text's summary does not match the tables: it lists ProtT5 among structure embeddings while the tables print ProstT5; it reports a GraphSAGE with DistMult result (+38% Hits@100 on HuRI) although DistMult is not a column; and the stated gains (for example ESM-2 with GraphSAGE +6% Hits@100 on OGBl-ppa) cannot be reproduced from the printed table values (GS random 0.0104, ESM-2 0.2421). Values are kept as printed; comparisons should rely on the tables only after a reviewer has judged this. Random-feature baselines on OGBl-ppa (GCN 0.0588, GS 0.0104 Hits@100) look far below the public OGB leaderboard values the authors say they reproduced (Supplementary Table S2, not retrieved; reviewer did not verify the leaderboard values).The role of protein embeddings for protein-protein interaction prediction with graph neural networks · Results and discussion, first paragraph; Tables 5 and 6 Methods, coverage and source GraphSAGE / ComplEx on OGBl-ppa (Accuracy) lit-interactions-20261011-ppiemb-protocol-ogbl-ppa-accuracy Aggregation: Not reported The role of protein embeddings for protein-protein interaction prediction with graph neural networks · Table 6, block 'OGBl-ppa', row 'GS', column 'ComplEx'
Configuration: GraphSAGE with ComplEx protein embeddings (function (knowledge-graph embedding)) (PPI embeddings study)	Protocol: OGBl-ppa PPI link prediction, Hits@100 (Table 5) Dataset: OGBl-ppa PPI graph (embedding study)	0.276 Hits at 100 (OGBl-ppa PPI link prediction) fraction · higher Uncertainty: Not reported by the source: Only a table-wide maximum SD is printed (Table 5/6 footnote) Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons The text's summary does not match the tables: it lists ProtT5 among structure embeddings while the tables print ProstT5; it reports a GraphSAGE with DistMult result (+38% Hits@100 on HuRI) although DistMult is not a column; and the stated gains (for example ESM-2 with GraphSAGE +6% Hits@100 on OGBl-ppa) cannot be reproduced from the printed table values (GS random 0.0104, ESM-2 0.2421). Values are kept as printed; comparisons should rely on the tables only after a reviewer has judged this. Random-feature baselines on OGBl-ppa (GCN 0.0588, GS 0.0104 Hits@100) look far below the public OGB leaderboard values the authors say they reproduced (Supplementary Table S2, not retrieved; reviewer did not verify the leaderboard values).The role of protein embeddings for protein-protein interaction prediction with graph neural networks · Results and discussion, first paragraph; Tables 5 and 6 Methods, coverage and source GraphSAGE / ComplEx on OGBl-ppa (Hits@100) lit-interactions-20261011-ppiemb-protocol-ogbl-ppa-hits-100 Aggregation: Not reported The role of protein embeddings for protein-protein interaction prediction with graph neural networks · Table 5, block 'OGBl-ppa', row 'GS', column 'ComplEx'
Configuration: GraphSAGE with majority vote across the representations (PPI embeddings study)	Protocol: OGBl-ppa PPI link prediction, Accuracy (Table 6) Dataset: OGBl-ppa PPI graph (embedding study)	0.882 Accuracy (OGBl-ppa PPI link prediction) fraction · higher Uncertainty: Not reported by the source: Only a table-wide maximum SD is printed (Table 5/6 footnote) Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Evidence concern: excluded from comparisons The text's summary does not match the tables: it lists ProtT5 among structure embeddings while the tables print ProstT5; it reports a GraphSAGE with DistMult result (+38% Hits@100 on HuRI) although DistMult is not a column; and the stated gains (for example ESM-2 with GraphSAGE +6% Hits@100 on OGBl-ppa) cannot be reproduced from the printed table values (GS random 0.0104, ESM-2 0.2421). Values are kept as printed; comparisons should rely on the tables only after a reviewer has judged this. Random-feature baselines on OGBl-ppa (GCN 0.0588, GS 0.0104 Hits@100) look far below the public OGB leaderboard values the authors say they reproduced (Supplementary Table S2, not retrieved; reviewer did not verify the leaderboard values).The role of protein embeddings for protein-protein interaction prediction with graph neural networks · Results and discussion, first paragraph; Tables 5 and 6 Methods, coverage and source GraphSAGE / Ensemble on OGBl-ppa (Accuracy) lit-interactions-20261011-ppiemb-protocol-ogbl-ppa-accuracy Aggregation: Not reported The role of protein embeddings for protein-protein interaction prediction with graph neural networks · Table 6, block 'OGBl-ppa', row 'GS', column 'Ensemble'

Source checking is not independent reproduction. Release 2026-10-22-1b7cf027e4f6.

Research readiness

0 of 4 readiness checks met. These checks assess whether the evidence supports a reproducible investigation; a source-checked score alone does not meet them.

Readiness checks, gaps and artifacts

Release 2026-10-22-1b7cf027e4f6 · Evidence verified: Not verified

Evidence incomplete

Replay metrics

Exact outcomes, predictions, identifiers and evaluator are connected.

Missing or unresolved evidence

No verified artifact manifest is linked to this exact record.
File checksums match the recorded files: not yet verified
Predictions are matched to the right samples: not yet verified
Score meaning and direction are confirmed: not yet verified
Metrics are recomputed from the saved predictions: not yet verified

Verified: Not verified

Evidence incomplete

Investigate discrepancies

Replay evidence includes annotations and an assessment of dependence. Unknown independence permits descriptive analysis only.

Missing or unresolved evidence

No verified artifact manifest is linked to this exact record.
File checksums match the recorded files: not yet verified
Predictions are matched to the right samples: not yet verified
Score meaning and direction are confirmed: not yet verified
Metrics are recomputed from the saved predictions: not yet verified
Sample annotations are recorded: not yet verified
Dependence between samples is assessed: not yet verified

Verified: Not verified

Evidence incomplete

Run locally

A pinned recipe describes the inputs, environment and resource requirements.

Missing or unresolved evidence

No verified artifact manifest is linked to this exact record.
File checksums match the recorded files: not yet verified
Predictions are matched to the right samples: not yet verified
Score meaning and direction are confirmed: not yet verified
A pinned run recipe exists: not yet verified
Compute requirements are estimated: not yet verified

Verified: Not verified

Evidence incomplete

Validate independently

Separate data and exposure records support an independent test.

Missing or unresolved evidence

No verified artifact manifest is linked to this exact record.
File checksums match the recorded files: not yet verified
Predictions are matched to the right samples: not yet verified
Score meaning and direction are confirmed: not yet verified
Independent validation data exist: not yet verified
Overlap with training data is checked: not yet verified

Verified: Not verified

Readiness describes the evidence in this release. Availability on your computer is checked separately when an investigation runs. Existing data exposure can prevent independent validation even when files are available.

Artifacts and reproduction

No verified artifact manifest is connected to this record yet. The gaps above identify what is needed before analysis can begin.

Dataset and evaluation context

A dataset supplies biological observations. The evaluation protocol defines how those observations are split, used and scored.

Evidence

Source checking verifies the cited claim or transcription. It does not establish independent reproduction.

Evidence table

Inspect claims, sources and review details

Trace each statement to its source and review. A context-only reference supports the record generally; it does not verify an individual field. Source checking does not reproduce an experiment.

One row per statement and cited source. Multiple citations are not independent evaluations. Shared locators are labelled explicitly.

6 evidence rows matching the loaded filters

Claims, original sources and review scope · Release 2026-10-22-1b7cf027e4f6
Property and statement	Original source and location	Review and provenance
Population 576 289 nodes; 30 326 273 edges Context-only references	The role of protein embeddings for protein-protein interaction prediction with graph neural networks Original source ↗ Table 4; Datasets Version: Briefings in Bioinformatics 2026, bbag313; PMC13280947 full-text XML Retrieved: 2026-10-11T21:27:23Z	not individually reviewed No individual claim review recorded Source has a recorded evidence concern. Consult its source page before using the claim. Audit details Field: `attributes.population` Source artifact SHA-256: `360d4f17b779e07f170c3f0c3755bfbfddd064831bf5a81d2642a4cfffd4866c` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Source location Table 4; Datasets Context-only references	The role of protein embeddings for protein-protein interaction prediction with graph neural networks Original source ↗ Table 4; Datasets Version: Briefings in Bioinformatics 2026, bbag313; PMC13280947 full-text XML Retrieved: 2026-10-11T21:27:23Z	not individually reviewed No individual claim review recorded Source has a recorded evidence concern. Consult its source page before using the claim. Audit details Field: `attributes.source_locator` Source artifact SHA-256: `360d4f17b779e07f170c3f0c3755bfbfddd064831bf5a81d2642a4cfffd4866c` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Split Not stated in the main text; leaderboard hyperparameters reproduced (Experimental design), so the OGB split is implied but not confirmed Context-only references	The role of protein embeddings for protein-protein interaction prediction with graph neural networks Original source ↗ Table 4; Datasets Version: Briefings in Bioinformatics 2026, bbag313; PMC13280947 full-text XML Retrieved: 2026-10-11T21:27:23Z	not individually reviewed No individual claim review recorded Source has a recorded evidence concern. Consult its source page before using the claim. Audit details Field: `attributes.split` Source artifact SHA-256: `360d4f17b779e07f170c3f0c3755bfbfddd064831bf5a81d2642a4cfffd4866c` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Version As used in bbag313 (OGB release for OGBl-ppa) Context-only references	The role of protein embeddings for protein-protein interaction prediction with graph neural networks Original source ↗ Table 4; Datasets Version: Briefings in Bioinformatics 2026, bbag313; PMC13280947 full-text XML Retrieved: 2026-10-11T21:27:23Z	not individually reviewed No individual claim review recorded Source has a recorded evidence concern. Consult its source page before using the claim. Audit details Field: `attributes.version` Source artifact SHA-256: `360d4f17b779e07f170c3f0c3755bfbfddd064831bf5a81d2642a4cfffd4866c` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Description Open Graph Benchmark ogbl-ppa protein association graph (576,289 nodes, 30,326,273 edges; STRING-derived, multi-species). Context-only references	The role of protein embeddings for protein-protein interaction prediction with graph neural networks Original source ↗ Table 4; Datasets Version: Briefings in Bioinformatics 2026, bbag313; PMC13280947 full-text XML Retrieved: 2026-10-11T21:27:23Z	not individually reviewed No individual claim review recorded Source has a recorded evidence concern. Consult its source page before using the claim. Audit details Field: `description` Source artifact SHA-256: `360d4f17b779e07f170c3f0c3755bfbfddd064831bf5a81d2642a4cfffd4866c` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Name OGBl-ppa PPI graph (embedding study) Context-only references	The role of protein embeddings for protein-protein interaction prediction with graph neural networks Original source ↗ Table 4; Datasets Version: Briefings in Bioinformatics 2026, bbag313; PMC13280947 full-text XML Retrieved: 2026-10-11T21:27:23Z	not individually reviewed No individual claim review recorded Source has a recorded evidence concern. Consult its source page before using the claim. Audit details Field: `name` Source artifact SHA-256: `360d4f17b779e07f170c3f0c3755bfbfddd064831bf5a81d2642a4cfffd4866c` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact

How to read the evidence

Sources and history

Release 2026-10-22-1b7cf027e4f6 · Record review: source checked

1 source record and release history

The role of protein embeddings for protein-protein interaction prediction with graph neural networks · Original source · Briefings in Bioinformatics 2026, bbag313; PMC13280947 full-text XML

Download this release (gzip)

Technical metadata and extraction receipts

Stable ID: lit-interactions-20261011-ppiemb-data-ogbl-ppa

areas: molecular-interactions; proteins-complexes
contexts: research
version: As used in bbag313 (OGB release for OGBl-ppa)
split: Not stated in the main text; leaderboard hyperparameters reproduced (Experimental design), so the OGB split is implied but not confirmed
population: 576 289 nodes; 30 326 273 edges
source locator: Table 4; Datasets

Related records

Suggest a correction