Understanding cellular responses to genetic perturbation is central to numerous biomedical
applications, from identifying genetic interactions involved in cancer to developing methods …

Recent advances in multiplexed single‐cell transcriptomics experiments facilitate the high‐
throughput study of drug and genetic perturbations. However, an exhaustive exploration of …

Background Genetic interaction profiles are highly informative and helpful for understanding
the functional linkages between genes, and therefore have been extensively exploited for …

How noncoding DNA determines gene expression in different cell types is a major unsolved
problem, and critical downstream applications in human genetics depend on improved …

Accurately modeling cellular response to perturbations is a central goal of computational
biology. While such modeling has been based on statistical, mechanistic and machine …

Gene regulation plays a fundamental role in shaping tissue identity, function, and response
to perturbation. Regulatory processes are controlled by complex networks of interacting …

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 …

Background Current technologies have lead to the availability of multiple genomic data
types in sufficient quantity and quality to serve as a basis for automatic global network …

Aberrant cell signaling can cause cancer and other diseases and is a focal point of drug
research. A common approach is to infer signaling activity of pathways from gene …

A deeper understanding of genetic regulation and functional mechanisms underlying
genetic associations with complex traits and diseases is impeded by cellular heterogeneity …