Single-cell technologies, particularly single-cell RNA sequencing (scRNA-seq) methods,
together with associated computational tools and the growing availability of public data …

Single-cell atlases promise to provide a 'missing link'between genes, diseases and
therapies. By identifying the specific cell types, states, programs and contexts where disease …

Predicting the effects of coding variants is a major challenge. While recent deep-learning
models have improved variant effect prediction accuracy, they cannot analyze all coding …

The development of neoplasia involves a complex and continuous interplay between
malignantly transformed cells and the tumour microenvironment (TME). Cancer …

Systematic evaluation of the impact of genetic variants is critical for the study and treatment
of human physiology and disease. While specific mutations can be introduced by genome …

Causal disentanglement seeks a representation of data involving latent variables that are
related via a causal model. A representation is identifiable if both the latent model and the …

Comprehensively charting the biologically causal circuits that govern the phenotypic space
of human cells has often been viewed as an insurmountable challenge. However, in the last …

Dimensionality reduction is standard practice for filtering noise and identifying relevant
features in large-scale data analyses. In biology, single-cell genomics studies typically begin …

The ever-growing compendium of genetic variants associated with human pathologies
demands new methods to study genotype–phenotype relationships in complex tissues in a …

Scalable sequence–function studies have enabled the systematic analysis and cataloging of
hundreds of thousands of coding and noncoding genetic variants in the human genome …