STRING-ing together protein complexes: Corpus and methods for extracting physical protein interactions from the biomedical literature.
Bioinformatics
; 2024 Sep 14.
Article
en En
| MEDLINE
| ID: mdl-39276156
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. 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 sub-network of the STRING database. This corpus comprises 1,287 documents with â¼3,500 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/. SUPPLEMENTARY INFORMATION Supplementary information are available at Bioinformatics online.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Idioma:
En
Revista:
Bioinformatics
Asunto de la revista:
INFORMATICA MEDICA
Año:
2024
Tipo del documento:
Article
País de afiliación:
Finlandia
Pais de publicación:
Reino Unido