Recent efforts to construct reference maps of cellular phenotypes have expanded the
volume and diversity of single-cell omics data, providing an unprecedented resource for …

High-throughput single-cell profiling provides an unprecedented ability to uncover the
molecular states of millions of cells. These technologies are, however, destructive to cells …

Abstract Large Language Models (LLMs) or foundation models have made significant
strides in both industrial and scientific domains. In this paper, we evaluate the performance …

Single-cell transcriptomics has broadened our understanding of cellular diversity and gene
expression dynamics in healthy and diseased tissues. Recently, spatial transcriptomics has …

Foundation model, which is pre-trained on broad data and is able to adapt to a wide range
of tasks, is advancing healthcare. It promotes the development of healthcare artificial …

James German's work to establish the natural history and cancer risk associated with Bloom
syndrome (BS) has had a strong influence on the generation of scientists and clinicians …

Cell states were traditionally defined by how they looked, where they were located, and what
functions they performed. In this post-genomic era, the field is largely focused on a …

We introduce Poseidon, a foundation model for learning the solution operators of PDEs. It is
based on a multiscale operator transformer, with time-conditioned layer norms that enable …

Integration of single-cell RNA-sequencing (scRNA-seq) datasets has become a standard
part of the analysis, with conditional variational autoencoders (cVAE) being among the most …

Transfer learning has revolutionized fields including natural language understanding and
computer vision by leveraging large‐scale general datasets to pretrain models with …