As spatially resolved multiplex profiling of RNA and proteins becomes more prominent, it is
increasingly important to understand the statistical power available to test specific …

Deep learning has transformed the way large and complex image datasets can be processed,
reshaping what is possible in bioimage analysis. As the complexity and size of bioimage …

Background For real-time monitoring of hospital patients, high-quality inference of patients’
health status using all information available from clinical covariates and lab test results is …

Single-cell RNA-sequencing (scRNA-seq) enables high-throughput measurement of RNA
expression in single cells. However, because of technical limitations, scRNA-seq data often …

Single-cell technologies characterize complex cell populations across multiple data modalities
at unprecedented scale and resolution. Multi-omic data for single cell gene expression, in …

We address the problem of regret minimization in logistic contextual bandits, where a learner
decides among sequential actions or arms given their respective contexts to maximize …

Integrating gene expression across tissues and cell types is crucial for understanding the
coordinated biological mechanisms that drive disease and characterize homoeostasis. …

Gene regulatory network inference is essential to uncover complex relationships among gene
pathways and inform downstream experiments, ultimately enabling regulatory network re-…

Motivation Identifying variants, both discrete and continuous, that are associated with
quantitative traits, or QTs, is the primary focus of quantitative genetics. Most current methods are …

Medical pathology images are visually evaluated by experts for disease diagnosis, but the
connection between image features and the state of the cells in an image is typically unknown…