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MLP on concatenated protein embeddings (protein-only baseline, no network) with RDF2Vec protein embeddings (function (knowledge-graph embedding)) (PPI embeddings 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

MLP on concatenated protein embeddings (protein-only baseline, no network) with RDF2Vec protein embeddings (function (knowledge-graph embedding)): MLP on concatenated protein embeddings (protein-only baseline, no network); node features: RDF2Vec protein embeddings (function (knowledge-graph embedding)).

4 evaluations · 4 results

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Evaluations and results

4 evaluations · 4 results. Different protocols are not a single leaderboard.

Sorted by Accuracy (HuRI PPI link prediction) (higher is better). The best value in each column is highlighted. Decimals are rounded for display; each value links to the printed value and its source.

Tested configuration	Protocol and dataset	Accuracy (HuRI PPI link prediction)	Accuracy (OGBl-ppa PPI link prediction)	Hits at 100 (HuRI PPI link prediction)	Hits at 100 (OGBl-ppa PPI link prediction)
MLP on concatenated protein embeddings (protein-only baseline, no network) with RDF2Vec protein embeddings (function (knowledge-graph embedding)) (PPI embeddings study)Evaluation	HuRI PPI link prediction, Accuracy (Table 6), HuRI PPI graph (embedding study)	0.699	Not reported	Not reported	Not reported
MLP on concatenated protein embeddings (protein-only baseline, no network) with RDF2Vec protein embeddings (function (knowledge-graph embedding)) (PPI embeddings study)Evaluation	HuRI PPI link prediction, Hits@100 (Table 5), HuRI PPI graph (embedding study)	Not reported	Not reported	0.347	Not reported
MLP on concatenated protein embeddings (protein-only baseline, no network) with RDF2Vec protein embeddings (function (knowledge-graph embedding)) (PPI embeddings study)Evaluation	OGBl-ppa PPI link prediction, Accuracy (Table 6), OGBl-ppa PPI graph (embedding study)	Not reported	0.832	Not reported	Not reported
MLP on concatenated protein embeddings (protein-only baseline, no network) with RDF2Vec protein embeddings (function (knowledge-graph embedding)) (PPI embeddings study)Evaluation	OGBl-ppa PPI link prediction, Hits@100 (Table 5), OGBl-ppa PPI graph (embedding study)	Not reported	Not reported	Not reported	0.21

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Tested configuration	Protocol and dataset	Finding	Evidence and details
Configuration: MLP on concatenated protein embeddings (protein-only baseline, no network) with RDF2Vec protein embeddings (function (knowledge-graph embedding)) (PPI embeddings study)	Protocol: HuRI PPI link prediction, Accuracy (Table 6) Dataset: HuRI PPI graph (embedding study)	0.699 Accuracy (HuRI 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 MLP on concatenated protein embeddings (protein-only baseline, no network) / RDF2Vec on HuRI (Accuracy) lit-interactions-20261011-ppiemb-protocol-huri-accuracy Aggregation: Not reported The role of protein embeddings for protein-protein interaction prediction with graph neural networks · Table 6, block 'HuRI', row 'MLP', column 'RDF2Vec'
Configuration: MLP on concatenated protein embeddings (protein-only baseline, no network) with RDF2Vec protein embeddings (function (knowledge-graph embedding)) (PPI embeddings study)	Protocol: HuRI PPI link prediction, Hits@100 (Table 5) Dataset: HuRI PPI graph (embedding study)	0.347 Hits at 100 (HuRI 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 MLP on concatenated protein embeddings (protein-only baseline, no network) / RDF2Vec on HuRI (Hits@100) lit-interactions-20261011-ppiemb-protocol-huri-hits-100 Aggregation: Not reported The role of protein embeddings for protein-protein interaction prediction with graph neural networks · Table 5, block 'HuRI', row 'MLP', column 'RDF2Vec'
Configuration: MLP on concatenated protein embeddings (protein-only baseline, no network) 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)	0.832 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 MLP on concatenated protein embeddings (protein-only baseline, no network) / 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 'MLP', column 'RDF2Vec'
Configuration: MLP on concatenated protein embeddings (protein-only baseline, no network) 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)	0.21 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	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 MLP on concatenated protein embeddings (protein-only baseline, no network) / 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 'MLP', column 'RDF2Vec'

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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

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Stable ID: lit-interactions-20261011-ppiemb-config-mlp-rdf2vec

areas: molecular-interactions; proteins-complexes
contexts: research
method types: supervised_machine_learning
reported name: MLP / RDF2Vec
foundation model eligible: false
parameters: MLP on concatenated protein embeddings (protein-only baseline, no network); node features: RDF2Vec protein embeddings (function (knowledge-graph embedding)). Leaderboard hyperparameters reproduced for OGBl-ppa (Experimental design).
source locator: Tables 5 and 6, row 'MLP', column 'RDF2Vec'
access reuse: Not stated in the source
missing metadata: version: reason: unreported; note: Embedding and GNN parameters are in Supplementary Tables S3, S5-S7, not retrieved
provenance note: MLP on concatenated protein embeddings (protein-only baseline, no network) using RDF2Vec protein embeddings (function (knowledge-graph embedding)), as evaluated for PPI prediction.

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