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 …

Deep Neural Networks (DNNs) trained on one dataset (source domain) do not perform well
on another set of data (target domain), which is different but has similar properties as the …

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 …

Despite recent advances in natural language processing, many statistical models for
processing text perform extremely poorly under domain shift. Processing biomedical and …

Abstract PubTator Central (https://www. ncbi. nlm. nih. gov/research/pubtator/) is a web
service for viewing and retrieving bioconcept annotations in full text biomedical articles …

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 …

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 …

In biomedical natural language processing, named entity recognition (NER) and named
entity normalization (NEN) are key tasks that enable the automatic extraction of biomedical …