Bayesian inference articles from across Nature Portfolio

Molecular changes in neurodegeneration occur much earlier than previously expected. In this study, dynamic molecular network analysis of iPSC differentiation uncovers a temporal pathway from histone to ISG15 with the earliest molecular changes of SCA1.

Sounds are generated by interactions between objects in the world. In this Review, Winkler and Denham describe how representations of sources and actions enable humans to segment complex auditory experience into meaningful units.

This protocol describes procedures for learning cellular and molecular processes from single-cell RNA-sequencing data using the non-negative matrix factorization algorithm Coordinated Gene Activity across Pattern Subsets. Parallel analysis is demonstrated in Python, R and GenePattern Notebook.

An outbreak of Mpox in the UK began in May 2022 and peaked in July. In this modelling study, the authors show that the decline in cases was likely due to behavioural changes among high-risk populations, whilst vaccination could prevent a rebound.

Inferring gene networks from discrete RNA counts across cells remains a complex problem. Following Bayesian non-parametrics, a computational framework is proposed to perform non-biased inference of transcription kinetics from single-cell RNA counting experiments.

Whole-genome sequencing (WGS) can be used to track the transmission of Neisseria gonorrhoeae in different sexual networks.

With the aid of informatics, microscopy is in the midst of a crucial evolution into a more quantitative and powerful technique.

To best interpret new sensory information, populations of sensory neurons must represent the lessons of past experience. How do they do this? The same solution to this problem is now reported in two very different sensory systems, providing a classic example of computational convergence.