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

In the field of biomedical text mining, the ability to extract relations from the literature is crucial for advancing both theoretical research and practical applications. There is a notable shortage of corpora designed to enhance the extraction of multiple types of relations, particularly focusing on proteins and protein-containing entities such as complexes and families, as well as chemicals. In this work, we present RegulaTome, a corpus that overcomes the limitations of several existing biomedical relation extraction (RE) corpora, many of which concentrate on single-type relations at the sentence level.

Overview of DrugProt task at BioCreative VII: data and methods for large-scale text mining and knowledge graph generation of heterogenous chemical-protein relations.

It is getting increasingly challenging to efficiently exploit drug-related information described in the growing amount of scientific literature. Indeed, for drug-gene/protein interactions, the challenge is even bigger, considering the scattered information sources and types of interactions. However, their systematic, large-scale exploitation is key for developing tools, impacting knowledge fields as diverse as drug design or metabolic pathway research.

S1000: a better taxonomic name corpus for biomedical information extraction.

Motivation: The recognition of mentions of species names in text is a critically important task for biomedical text mining. While deep learning-based methods have made great advances in many named entity recognition tasks, results for species name recognition remain poor. We hypothesize that this is primarily due to the lack of appropriate corpora.

Automatic extraction of brain surface and mid-sagittal plane from PET images applying deformable models.

In this study, we propose and evaluate new methods for automatic extraction of the brain surface and the mid-sagittal plane from functional positron emission tomography (PET) images. Designing methods for these segmentation tasks is challenging because the spatial distribution of intensity values in a PET image depends on the applied radiopharmaceutical and the contrast to noise ratio in a PET image is typically low. We extracted the brain surface with a deformable model which is based on a global optimization algorithm.