Artificial intelligence (AI) systems utilizing deep neural networks and machine learning (ML)
algorithms are widely used for solving critical problems in bioinformatics, biomedical …

The COVID-19 (coronavirus disease 2019) pandemic has had a significant impact on
society, both because of the serious health effects of COVID-19 and because of public …

AI is undergoing a paradigm shift with the rise of models (eg, BERT, DALL-E, GPT-3) that are
trained on broad data at scale and are adaptable to a wide range of downstream tasks. We …

Developing personalized diagnostic strategies and targeted treatments requires a deep
understanding of disease biology and the ability to dissect the relationship between …

Gene and protein set enrichment analysis is a critical step in the analysis of data collected
from omics experiments. Enrichr is a popular gene set enrichment analysis web-server …

The science of science has attracted growing research interests, partly due to the increasing
availability of large-scale datasets capturing the innerworkings of science. These datasets …

Artificial intelligence (AI) models trained on published scientific findings have been used to
invent valuable materials and targeted therapies, but they typically ignore the human …

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

Prompt tuning with pre-trained language models (PLM) has exhibited outstanding
performance by reducing the gap between pre-training tasks and various downstream …

Background: Knowledge is evolving over time, often as a result of new discoveries or
changes in the adopted methods of reasoning. Also, new facts or evidence may become …