Bayesian statistics is an approach to data analysis based on Bayes' theorem, where
available knowledge about parameters in a statistical model is updated with the information …

Modern deep learning methods constitute incredibly powerful tools to tackle a myriad of
challenging problems. However, since deep learning methods operate as black boxes, the …

Spatial transcriptomic technologies promise to resolve cellular wiring diagrams of tissues in
health and disease, but comprehensive mapping of cell types in situ remains a challenge …

Over the last decade, neural networks have reached almost every field of science and
become a crucial part of various real world applications. Due to the increasing spread …

Increasing evidence suggests that the gut microbiome may modulate the efficacy of cancer
immunotherapy. In a B cell lymphoma patient cohort from five centers in Germany and the …

Repeated emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)
variants with increased fitness underscores the value of rapid detection and characterization …

To the Editor—Methods for analyzing single-cell data 1, 2, 3, 4 perform a core set of
computational tasks. These tasks include dimensionality reduction, cell clustering, cell-state …

Locating 3D objects from a single RGB image via Perspective-n-Points (PnP) is a long-
standing problem in computer vision. Driven by end-to-end deep learning, recent studies …

Bayesian optimization provides sample-efficient global optimization for a broad range of
applications, including automatic machine learning, engineering, physics, and experimental …

Smarter applications are making better use of the insights gleaned from data, having an
impact on every industry and research discipline. At the core of this revolution lies the tools …