Oscar William Lithgow-Serrano

This paper uses machine learning to extract arthropod trait data from literature, creating a database to support biodiversity research.

Lisen&Curate is a system that aids manual curation of bacterial transcription initiation regulation by integrating text mining and sentence similarity metrics to accelerate literature review and knowledge extraction.

The paper introduces L-Regulon, a soft-curation approach using semantic enrichment to improve knowledge extraction from scientific literature, specifically applied to RegulonDB data.

The paper creates a similarity corpus for microbial transcriptional regulation sentences, evaluated by curators to aid in biomedical literature curation.