STRING-ing together protein complexes: corpus and methods for extracting physical protein interactions from the biomedical literature.

{"sci_version": "1.0", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39276156", "kind": "science-record", "revision": 2, "content_hash": "sha256:a85323149265206ac848b86839a47208d08a24768fce04dde958ead135ddcaf5", "created_at": "2026-11-01T20:29:26Z", "updated_at": "2026-11-01T21:33:54Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "STRING-ing together protein complexes: corpus and methods for extracting physical protein interactions from the biomedical literature.", "doi": "10.1093/bioinformatics/btae552", "pmid": "39276156", "authors": ["Farrokh Mehryary", "Katerina Nastou", "Tomoko Ohta", "Lars Juhl Jensen", "Sampo Pyysalo"], "journal": "Bioinformatics (Oxford, England)", "year": 2024, "abstract": "MOTIVATION: Understanding biological processes relies heavily on curated knowledge of physical interactions between proteins. Yet, a notable gap remains between the information stored in databases of curated knowledge and the plethora of interactions documented in the scientific literature.\nRESULTS: To bridge this gap, we introduce ComplexTome, a manually annotated corpus designed to facilitate the development of text-mining methods for the extraction of complex formation relationships among biomedical entities targeting the downstream semantics of the physical interaction subnetwork of the STRING database. This corpus comprises 1287 documents with ∼3500 relationships. We train a novel relation extraction model on this corpus and find that it can highly reliably identify physical protein interactions (F1-score = 82.8%). We additionally enhance the model's capabilities through unsupervised trigger word detection and apply it to extract relations and trigger words for these relations from all open publications in the domain literature. This information has been fully integrated into the latest version of the STRING database.\nAVAILABILITY AND IMPLEMENTATION: We provide the corpus, code, and all results produced by the large-scale runs of our systems biomedical on literature via Zenodo https://doi.org/10.5281/zenodo.8139716, Github https://github.com/farmeh/ComplexTome_extraction, and the latest version of STRING database https://string-db.org/.", "claims": [{"id": "claim-method", "statement": "As shown in Table 2, while the baseline system with 75.3% average F1-score has a moderately good performance on the task, it is 9 percentage points behind the main relation extraction system (84.3% average F1-score).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39276156", "locator": "sections[19] \"3.2.2 Baseline evaluation and comparison\" paragraph 4", "quote": "As shown in Table 2, while the baseline system with 75.3% average F1-score has a moderately good performance on the task, it is 9 percentage points behind the main relation extraction system (84.3% average F1-score)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All resources introduced in this study are available under open licenses through Zenodo (https://doi.org/10.5281/zenodo.8139716) and GitHub (https://github.com/farmeh/ComplexTome_extraction).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39276156", "locator": "sections[32] \"Data availability\" paragraph 2", "quote": "All resources introduced in this study are available under open licenses through Zenodo (https://doi.org/10.5281/zenodo.8139716) and GitHub (https://github.com/farmeh/ComplexTome_extraction)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "We train a novel relation extraction model on this corpus and find that it can highly reliably identify physical protein interactions (F1-score = 82.8%).", "kind": "quantitative", "target": {"value": 82.8, "unit": "per cent", "tolerance": null, "source": "We train a novel relation extraction model on this corpus and find that it can highly reliably identify physical protein interactions (F1-score = 82.8%)."}, "evidence": [{"asset": "source:pmid:39276156", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "We train a novel relation extraction model on this corpus and find that it can highly reliably identify physical protein interactions (F1-score = 82.8%)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "We train a novel relation extraction model on this corpus and find that it can highly reliably identify physical protein interactions (F1-score = 82.8%).", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:39276156"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "blocked", "blocked_on": "data:accession_absent", "description": "The GEO series accession resolves and its sample accessions are listed. 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