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Updated: Jun 13, 2025

Protein Complex Affinity Capture from Cryomilled Mammalian Cells
Published on: December 9, 2016
STRING-ing together protein complexes: corpus and methods for extracting physical protein interactions from the
Farrokh Mehryary1, Katerina Nastou2, Tomoko Ohta3
1TurkuNLP Group, Department of Computing, University of Turku, Turku 20014, Finland.
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.
Results:
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.
Availability 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/.
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