Efficiently accelerated bioimage analysis with NanoPyx, a Liquid Engine-powered Python framework
Nat Methods. 2025 Feb;22(2):283-286. doi: 10.1038/s41592-024-02562-6. Epub 2025 Jan 2.
Published on January 2, 2025
ABSTRACT
The expanding scale and complexity of microscopy image datasets require accelerated analytical workflows. NanoPyx meets this need through an adaptive framework enhanced for high-speed analysis. At the core of NanoPyx, the Liquid Engine dynamically generates optimized central processing unit and graphics processing unit code variations, learning and predicting the fastest based on input data and hardware. This data-driven optimization achieves considerably faster processing, becoming broadly relevant to reactive microscopy and computing fields requiring efficiency.
PMID:39747509 | PMC:PMC11810771 | DOI:10.1038/s41592-024-02562-6
Latest Publications
- Author response to “Commentary on detoxification of deoxynivalenol by pathogen-inducible tau-class glutathione transferases from wheat” by Dr. Latika Shendre
- Editorial: Epigenetic regulation of T cell function in type 1 diabetes
- Prenatal exposure to persistent organic pollutants modulates the metabolism and gut microbiota of the offspring
- Preventing Proteomics Data Tombs Through Collective Responsibility and Community Engagement
- Cell viscosity influences haematogenous dissemination and metastatic extravasation of tumour cells