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 …

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

Developing a universal representation of cells which encompasses the tremendous
molecular diversity of cell types within the human body and more generally, across species …

The advent and success of foundation models such as GPT has sparked growing interest in
their application to single-cell biology. Models like Geneformer and scGPT have emerged …

The transformers have achieved significant accomplishments in the natural language
processing as its outstanding parallel processing capabilities and highly flexible attention …

Deep learning (DL) has shown potential to provide powerful representations of bulk RNA-
seq data in cancer research. However, there is no consensus regarding the impact of design …

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 …

Large language models (LLMs) are a class of artificial intelligence models based on deep
learning, which have great performance in various tasks, especially in natural language …

The great capability of AI large language models (LLMs) pre-trained on massive natural
language data has inspired scientists to develop a few …

Single-cell RNA sequencing (scRNA-seq) is widely used to reveal heterogeneity in cells,
which has given us insights into cell-cell communication, cell differentiation, and differential …