The exponential growth of big data in RNA biology (RB) has led to the development of deep
learning (DL) models that have driven crucial discoveries. As constantly evidenced by DL …

As the field of single-cell transcriptomics matures, research is shifting focus from
phenomenological descriptions of cellular phenotypes to a mechanistic understanding of the …

Gene regulation plays a critical role in the cellular processes that underlie human health and
disease. The regulatory relationship between transcription factors (TFs), key regulators of …

Recent advances in single-cell technologies have led to the discovery of thousands of brain
cell types; however, our understanding of the gene regulatory programs in these cell types is …

Existing methods for gene regulatory network (GRN) inference rely on gene expression data
alone or on lower resolution bulk data. Despite the recent integration of chromatin …

We introduce GRouNdGAN, a gene regulatory network (GRN)-guided reference-based
causal implicit generative model for simulating single-cell RNA-seq data, in silico …

Inferring gene regulatory networks (GRNs) is a fundamental challenge in biology that aims
to unravel the complex relationships between genes and their regulators. Deciphering these …

Recent advances in single-cell omics have transformed characterisation of cell types in
challenging-to-study biological contexts. In contexts with limited single-cell samples, such as …

Single‐cell mass spectrometry (MS) is significant in biochemical analysis and holds great
potential in biomedical applications. Efficient sample preparation like sorting (ie, separating …

The immune system comprises diverse specialized cell types that cooperate to defend the
host against a wide range of pathogenic threats. Recent advancements in single-cell and …