Abstract
Motivation: Despite significant progress in biomedical information extraction, there is a lack of resources for Named Entity Recognition (NER) and Named Entity Normalization (NEN) of protein-containing complexes. Current resources inadequately address the recognition of protein-containing complex names across different organisms, underscoring the crucial need for a dedicated corpus. Results: We introduce the Complex Named Entity Corpus (CoNECo), an annotated corpus for NER and NEN of complexes. CoNECo comprises 1621 documents with 2052 entities, 1976 of which are normalized to Gene Ontology. We divided the corpus into training, development, and test sets and trained both a transformer-based and dictionary-based tagger on them. Evaluation on the test set demonstrated robust performance, with F-scores of 73.7% and 61.2%, respectively. Subsequently, we applied the best taggers for comprehensive tagging of the entire openly accessible biomedical literature.
| Originalsprog | Engelsk |
|---|---|
| Artikelnummer | vbae116 |
| Tidsskrift | Bioinformatics Advances |
| Vol/bind | 4 |
| Udgave nummer | 1 |
| Antal sider | 7 |
| DOI | |
| Status | Udgivet - 2024 |
Bibliografisk note
Funding Information:This work was supported by the Novo Nordisk Foundation [NNF14CC0001, NNF20SA0035590 to M.K.], the Academy of Finland [332844], and the European Union\u2019s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie [101023676 to K.N.].
Funding Information:
We thank the CSC-IT Center for Science for generous computational resources. This work was supported by the Novo Nordisk Foundation [NNF14CC0001, NNF20SA0035590 to M.K.], the Academy of Finland [332844], and the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie [101023676 to K.N.].
Publisher Copyright:
© 2024 The Author(s).