Tomoko Ohta

The paper introduces a manually annotated corpus and a relation extraction model to identify physical protein interactions from biomedical literature, integrating results into the STRING database.

RegulaTome provides a comprehensive corpus of biomedical relations, enabling improved relation extraction with a Transformer model achieving 66.6% F1-score.

The paper introduces S1000, a new corpus for species name recognition in biomedical text, achieving high accuracy with both deep learning and dictionary-based methods.