Artificial intelligence (AI) models based on deep learning now represent the state of the art
for making functional predictions in genomics research. However, the underlying basis on …

Large Language Models (LLMs) have emerged as a transformative power in enhancing
natural language comprehension, representing a significant stride toward artificial general …

Genomic (DNA) sequences encode an enormous amount of information for gene regulation
and protein synthesis. Similar to natural language models, researchers have proposed …

The prediction of molecular phenotypes from DNA sequences remains a longstanding
challenge in genomics, often driven by limited annotated data and the inability to transfer …

The genome is a sequence that encodes the DNA, RNA, and proteins that orchestrate an
organism's function. We present Evo, a long-context genomic foundation model with a …

Proteins play a pivotal role in biological systems. The use of machine learning algorithms for
protein classification can assist and even guide biological experiments, offering crucial …

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 …

How noncoding DNA determines gene expression in different cell types is a major unsolved
problem, and critical downstream applications in human genetics depend on improved …

Near out-of-distribution detection (OOD) is a major challenge for deep neural networks. We
demonstrate that large-scale pre-trained transformers can significantly improve the state-of …

Here, we present DeepBIO, the first-of-its-kind automated and interpretable deep-learning
platform for high-throughput biological sequence functional analysis. DeepBIO is a one-stop …