Method

RDF2Vec

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

RDF2Vec random-walk knowledge-graph embedding method.

Results are recorded on linked configurations and versions of this method. Their settings and evaluations are kept separate below.

7 configurations with results

Results on linked records

This method has no results of its own in this release. 7 linked records have results. They are not assigned to the underlying method: each record keeps its own settings and evaluations, and its scores are on its own page.

GCN with RDF2Vec protein embeddings (function (knowledge-graph embedding)) (PPI embeddings study) Configuration · uses this model · 4 evaluations · 4 results
GraphSAGE with RDF2Vec protein embeddings (function (knowledge-graph embedding)) (PPI embeddings study) Configuration · uses this model · 4 evaluations · 4 results
MLP on concatenated protein embeddings (protein-only baseline, no network) with RDF2Vec protein embeddings (function (knowledge-graph embedding)) (PPI embeddings study) Configuration · uses this model · 4 evaluations · 4 results
NGNN with GCN combined with SEAL with RDF2Vec protein embeddings (function (knowledge-graph embedding)) (PPI embeddings study) Configuration · uses this model · 4 evaluations · 4 results
NGNN with GCN with RDF2Vec protein embeddings (function (knowledge-graph embedding)) (PPI embeddings study) Configuration · uses this model · 4 evaluations · 4 results
NGNN with GraphSAGE combined with SEAL with RDF2Vec protein embeddings (function (knowledge-graph embedding)) (PPI embeddings study) Configuration · uses this model · 4 evaluations · 4 results
NGNN with GraphSAGE with RDF2Vec protein embeddings (function (knowledge-graph embedding)) (PPI embeddings study) Configuration · uses this model · 4 evaluations · 4 results

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Claims, original sources and review scope · Release 2026-10-22-1b7cf027e4f6
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Release 2026-10-22-1b7cf027e4f6 · Record review: source checked

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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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Technical metadata and extraction receipts

Stable ID: lit-interactions-20261011-ppiemb-method-rdf2vec

areas: molecular-interactions; proteins-complexes
contexts: research
method types: supervised_machine_learning
reported name: RDF2Vec
entity level: method
source locator: Experimental design; Table 5 and 6 column headers
missing metadata: version: reason: unreported; note: No checkpoint or version printed in the main text; parameters are in Supplementary Table S3, not retrieved

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