Computational platforms and environments articles from across Nature Portfolio

Giotto Suite provides a comprehensive, flexible and scalable platform for technology-agnostic spatial omics analysis using R.

Cross-modality mapping extends to 3D with image-varifold based technologies for partial matching, spatial resampling, and feature selection to efficiently represent and integrate high-dimensional spatial-omics data with tissue-scale imaging.

Metadata annotation is crucial for data analysis, however it is often overlooked when depositing proteomics data. Here, the authors introduce Sample and Data Relationship Format (SDRF)-compliant metadata integration feature in MaxQuant, a widely used software for proteomics data analysis.

Quantum computing is a promising technology to solve complex challenges that would take classical computers an impractical amount of time. This Perspective discusses the current state of quantum computing and possible applications in enzyme engineering and biocatalysis.

We present a cost-effective ultra-high-throughput cytometry-based framework for the detection of physical interactions between cells, along with the characterization of complex cellular landscapes. Application of our approach can offer a systems-level understanding of immunity and facilitate study of the kinetics, mode of action and personalized response prediction of immunotherapies.

A unified optimization framework, CORNETO, introduces a versatile approach to knowledge-driven biological network inference, bringing machine learning sensibilities to systems biology.

A recent study assesses bias in artificial intelligence (AI)-generated medical language to find differences in age, sex, and ethnicity. An optimization technique is proposed to improve fairness without sacrificing performance.