Sampo Pyysalo

Lifestyle factors in the biomedical literature: An ontology and comprehensive resources for named entity recognition

The paper develops a Lifestyle Factor Ontology and annotated corpus for detecting lifestyle factors in biomedical texts, achieving 76% F-score with a transformer-based NER system.

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

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.

Improving dictionary-based named entity recognition with deep learning

The paper improves dictionary-based named entity recognition by using a Transformer model to automatically generate block lists, increasing precision in text mining.

CoNECo: A Corpus for Named Entity recognition and normalization of protein Complexes

The paper introduces CoNECo, a corpus for NER and NEN of protein complexes, achieving F1-scores of 73.7% and 61.2% with transformer-based and dictionary-based taggers, respectively.

RegulaTome: a corpus of typed, directed, and signed relations between biomedical entities in the scientific literature

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

S1000: A better taxonomic name corpus for biomedical information extraction

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.