Pre-trained language models (PLMs) have been the de facto paradigm for most natural
language processing tasks. This also benefits the biomedical domain: researchers from …

Automated relation extraction (RE) from biomedical literature is critical for many downstream
text mining applications in both research and real-world settings. However, most existing …

Abstract Information overload is a major obstacle to scientific progress. The explosive growth
in scientific literature and data has made it ever harder to discover useful insights in a large …

Implementing precision medicine hinges on the integration of omics data, such as
proteomics, into the clinical decision-making process, but the quantity and diversity of …

Motivation Biomedical text mining is becoming increasingly important as the number of
biomedical documents rapidly grows. With the progress in natural language processing …

Abstract miRBase catalogs, names and distributes microRNA gene sequences. The latest
release of miRBase (v22) contains microRNA sequences from 271 organisms: 38 589 …

Abstract Recently, Large Language Models (LLMs) have demonstrated impressive
capability to solve a wide range of tasks. However, despite their success across various …

Abstract PubTator 3.0 (https://www. ncbi. nlm. nih. gov/research/pubtator3/) is a biomedical
literature resource using state-of-the-art AI techniques to offer semantic and relation …

In this report, we introduce SciFive, a domain-specific T5 model that has been pre-trained on
large biomedical corpora. Our model outperforms the current SOTA methods (ie BERT …

Motivation Text mining has become an important tool for biomedical research. The most
fundamental text-mining task is the recognition of biomedical named entities (NER), such as …