Biomedical relation extraction can automatically extract high-quality biomedical relations
from biomedical texts, which is a vital step for the mining of biomedical knowledge hidden in …

Motivation: To minimize the accelerating amount of time invested on the biomedical
literature search, numerous approaches for automated knowledge extraction have been …

Motivation Entity relation extraction is one of the fundamental tasks in biomedical text
mining, which is usually solved by the models from natural language processing. Compared …

Extracting biological interactions from published literature helps us understand complex
biological systems, accelerate research, and support decision-making in drug or treatment …

In this paper, we propose a recurrent neural network model for identifying protein-protein
interactions in biomedical literature. Experiments on two largest public benchmark datasets …

Abstract Information about the interactions between chemical compounds and proteins is
indispensable for understanding the regulation of biological processes and the development …

Abstract Motivation Natural Language Processing techniques are constantly being
advanced to accommodate the influx of data as well as to provide exhaustive and structured …

Identifying interactions between proteins is important to understand underlying biological
processes. Extracting a protein-protein interaction (PPI) from the raw text is often very …

Background Relationships between bio-entities (genes, proteins, diseases, etc.) constitute a
significant part of our knowledge. Most of this information is documented as unstructured text …

Automatic extraction of protein-protein interaction (PPI) pairs from biomedical literature is a
widely examined task in biological information extraction. Currently, many kernel based …