Brielin Brown

Abstract Inference of directed biological networks is an important but notoriously challenging problem. The recent proliferation of large-scale CRISPR perturbation data provides a new opportunity to tackle this problem by leveraging the transcriptional response to the presence of a gene-targeting guide. Here, we introduce inverse sparse regression (inspre), an approach to learning causal networks that leverages large-scale intervention-response data.

• None C9orf72 is required for proper macrophage and microglial function in mice. : • None Neutrophils and macrophages: the main partners of phagocyte cell systems. : • None DNA methylation changes of whole blood cells in response to active smoking exposure in adults: a systematic review of DNA methylation studies. : • None Natural variation in the parameters of innate immune cells is preferentially driven by genetic factors. : • None Effects of Ageing on the Immune System: Infants to Elderly.

Abstract Inference of directed biological networks is an important but notoriously challenging problem. We introduce inverse sparse regression (inspre), an approach to learning causal networks that leverages large-scale intervention-response data. Applied to 788 genes from the genome-wide perturb-seq dataset, inspre helps elucidate the network architecture of blood traits. Competing Interest Statement Tuuli Lappalainen is a paid adviser or consultant of Variant Bio and GSK.