Efficiently accelerated bioimage analysis with NanoPyx, a Liquid Engine-powered Python framework

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dc.contributor.authorSaraiva, Bruno M.
dc.contributor.authorCunha, Inês
dc.contributor.authorBrito, António D.
dc.contributor.authorFollain, Gautier
dc.contributor.authorPortela, Raquel
dc.contributor.authorHaase, Robert
dc.contributor.authorPereira, Pedro M.
dc.contributor.authorJacquemet, Guillaume
dc.contributor.authorHenriques, Ricardo
dc.contributor.organizationfi=Turun biotiedekeskus|en=Turku Bioscience Centre|
dc.contributor.organization-code1.2.246.10.2458963.20.18586209670
dc.converis.publication-id484850987
dc.converis.urlhttps://research.utu.fi/converis/portal/Publication/484850987
dc.date.accessioned2025-08-27T22:38:57Z
dc.date.available2025-08-27T22:38:57Z
dc.description.abstract<p>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.<br></p>
dc.format.pagerange283
dc.format.pagerange286
dc.identifier.eissn1548-7105
dc.identifier.jour-issn1548-7091
dc.identifier.olddbid202541
dc.identifier.oldhandle10024/185568
dc.identifier.urihttps://www.utupub.fi/handle/11111/47502
dc.identifier.urlhttps://doi.org/10.1038/s41592-024-02562-6
dc.identifier.urnURN:NBN:fi-fe2025082785760
dc.language.isoen
dc.okm.affiliatedauthorFollain, Gautier
dc.okm.affiliatedauthorJacquemet, Guillaume
dc.okm.discipline220 Industrial biotechnologyen_GB
dc.okm.discipline220 Teollinen bioteknologiafi_FI
dc.okm.internationalcopublicationinternational co-publication
dc.okm.internationalityInternational publication
dc.okm.typeA1 ScientificArticle
dc.publisherSpringer Nature
dc.publisher.countryUnited Statesen_GB
dc.publisher.countryYhdysvallat (USA)fi_FI
dc.publisher.country-codeUS
dc.relation.doi10.1038/s41592-024-02562-6
dc.relation.ispartofjournalNature Methods
dc.relation.volume22
dc.source.identifierhttps://www.utupub.fi/handle/10024/185568
dc.titleEfficiently accelerated bioimage analysis with NanoPyx, a Liquid Engine-powered Python framework
dc.year.issued2025

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