Foundation models (FMs) are large-scale deeplearning models that are developed using
large datasets and self-supervised learning methods. These models serve as a base for …

Foundation models for natural language processing, powered by the transformer
architecture, exhibit remarkable in-context learning (ICL) capabilities, allowing pre-trained …

Genomic sequence-to-activity models are increasingly utilized to understand gene
regulatory syntax and probe the functional consequences of regulatory variation. Current …

Prevalent in biological applications (eg, human phenotype measurements), multimodal
datasets can provide valuable insights into the underlying biological mechanisms. However …

In biology, messenger RNA (mRNA) plays a crucial role in gene expression and protein
synthesis. Accurate predictive modeling of mRNA properties can greatly enhance our …

Genomic Language Models (gLMs), encompassing DNA models, RNA models, and
multimodal models, are becoming widely used for the analysis of biological sequences …