Biological systems are composed of a vast web of multiscale molecular interactors and
interactions. High-throughput technologies, both bulk and single cell, now allow for …

Cell type-specific gene expression patterns are outputs of transcriptional gene regulatory
networks (GRNs) that connect transcription factors and signaling proteins to target genes …

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 …

Cellular gene expression changes throughout a dynamic biological process, such as
differentiation. Pseudotimes estimate cells' progress along a dynamic process based on …

Gene regulatory networks (GRNs) serve as useful abstractions to understand transcriptional
dynamics in developmental systems. Computational prediction of GRNs has been …

Among the biggest challenges in the post-GWAS (genome-wide association studies) era is
the interpretation of disease-associated genetic variants in non-coding genomic regions …

Cancers are shaped by somatic mutations, microenvironment, and patient background, each
altering gene expression and regulation in complex ways, resulting in heterogeneous …

Computer simulations of cell behaviors and dynamics allow for investigation of aspects of
cellular biology with a ground truth that is currently difficult or impossible to generate from …

In recent years, many algorithms for inferring gene regulatory networks from single-cell
transcriptomic data have been published. Several studies have evaluated their accuracy in …

Population-scale single cell RNA-seq (scRNA-seq) datasets create unique opportunities for
quantifying expression variation across individuals at the gene co-expression network level …