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 …

Disease diagnosis represents a critical and arduous endeavor within the medical field.
Artificial intelligence (AI) techniques, spanning from machine learning and deep learning to …

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 …

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 …

Abstract Foundation Models (FMs) have made significant strides in both industrial and
scientific domains. In this paper, we evaluate the performance of FMs for single-cell …

Tissue makeup relies fundamentally on the cellular microenvironment. Spatial single-cell
genomics allows probing the underlying cellular interactions in an unbiased, scalable …

Despite the wealth of single-cell multi-omics data, it remains challenging to predict the
consequences of novel genetic and chemical perturbations in the human body. It requires …

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

Researching genes and their interactions is crucial for deciphering the fundamental laws of
cellular activity, advancing disease treatment, drug discovery, and more. Large language …