Machine learning articles from across Nature Portfolio

Most features of a cell are determined by gene programs — sets of co-expressed genes that execute a specific function. By incorporating existing knowledge about gene programs and cell types, the Spectra factor analysis method improves how we decode single-cell transcriptomic data and offers insights into challenging tumor immune contexts.

The detection of mobile genetic elements is crucial for exploring the ecology and evolution of microbial communities, and it has diverse implications in biotechnology and public health. geNomad is a computational framework that enables researchers to precisely identifiy and annotate plasmids and viruses in sequencing data on a large scale.

Using pathology data from Twitter, researchers have built a visual-language model for classifying and retrieving histopathology images — representing a milestone in the development of multifunctional foundational artificial intelligence models in computational pathology.

Multi-omics data integration can be challenging in the event of cell heterogeneity. Here, the authors present scBridge, a method that exploits heterogeneous omics differences, to progressively integrate cells and narrows omics gap, leading to promising integration and label transfer results.

Deep learning-based models for tracking behavior are often constrained by manual annotation. Here, authors present GlowTrack, an approach using fluorescence to generate large and diverse training sets that improve model robustness and tracking coverage.

A new Nature series will explore the many ways in which artificial intelligence is changing science — for better and for worse.

When it comes to large language models, there’s one for every occasion. Find the most appropriate match for you in our AI speed-dating feature.

Large language models and other artificial-intelligence systems could be excellent at synthesizing scientific evidence for policymakers — but only with appropriate safeguards and humans in the loop.

A Nature survey finds that scientists are concerned, as well as excited, by the increasing use of artificial-intelligence tools in research.

A Nature special on how AI is transforming the scientific enterprise.

Deceptive videos and images created using generative AI could sway elections, crash stock markets and ruin reputations. Researchers are developing methods to limit their harm.