Chunsheng Zhang

TL;DR: The causal network structure is a useful predictor of response to gene perturbations and presents a framework to test models of disease mechanisms underlying LOAD.

TL;DR: The potential for TMB and a T cell–inflamed GEP to jointly predict clinical response to pembrolizumab was assessed in >300 patient samples with advanced solid tumors and melanoma across 22 tumor types from four KEYNOTE clinical trials.

TL;DR: It is shown that this approach can predict transcriptional responses to single gene–perturbation experiments using gene-expression data in the context of a segregating mouse population and the utility of this approach is demonstrated by identifying and experimentally validating the involvement of three new genes in susceptibility to obesity.

TL;DR: Application of this method to liver and adipose gene expression data generated from a segregating mouse population results in the identification of a macrophage-enriched network supported as having a causal relationship with disease traits associated with metabolic syndrome.

TL;DR: Evidence is reported that suggests that the number of HBV integrations is associated with patient survival and copy-number variations were significantly increased at HBV breakpoint locations where chromosomal instability was likely induced.