Generative pretrained models have achieved remarkable success in various domains such
as language and computer vision. Specifically, the combination of large-scale diverse …

Large pretrained models have become foundation models leading to breakthroughs in
natural language processing and related fields. Developing foundation models for …

Mapping gene networks requires large amounts of transcriptomic data to learn the
connections between genes, which impedes discoveries in settings with limited data …

As the number of single‐cell transcriptomics datasets grows, the natural next step is to
integrate the accumulating data to achieve a common ontology of cell types and states …

A common approach to benchmarking of single-cell transcriptomics tools is to generate
synthetic datasets that statistically resemble experimental data. However, most existing …

In the short few months since the release of ChatGPT 1, 2, the potential for large language
models (LLMs) and generative artificial intelligence (AI) to disrupt fields as diverse as art …

Generative models provide a well‐established statistical framework for evaluating
uncertainty and deriving conclusions from large data sets especially in the presence of …

Deep learning architectures such as variational autoencoders have revolutionized the
analysis of transcriptomics data. However, the latent space of these variational …

The advent of single-cell RNA sequencing (scRNA-seq) technologies has revolutionized
transcriptomic studies. However, large-scale integrative analysis of scRNA-seq data remains …

Single-cell transcriptome measurements can reveal unexplored biological diversity, but they
suffer from technical noise and bias that must be modeled to account for the resulting …