{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T22:21:53Z","timestamp":1790029313975,"version":"4.0.1"},"reference-count":27,"publisher":"Oxford University Press (OUP)","issue":"Supplement_2","license":[{"start":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T00:00:00Z","timestamp":1787529600000},"content-version":"vor","delay-in-days":23,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Leducq Foundation Transatlantic Network of Excellence","award":["19CVD01"],"award-info":[{"award-number":["19CVD01"]}]},{"DOI":"10.13039\/501100000265","name":"Medical Research Council","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000265","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Centre of Research Excellence in Exposome Immunology","award":["UKRI\/MR\/B000935\/1"],"award-info":[{"award-number":["UKRI\/MR\/B000935\/1"]}]},{"name":"MRC Doctoral Training Programme"},{"DOI":"10.13039\/100010269","name":"Wellcome Trust","doi-asserted-by":"publisher","award":["227415\/Z\/23\/Z"],"award-info":[{"award-number":["227415\/Z\/23\/Z"]}],"id":[{"id":"10.13039\/100010269","id-type":"DOI","asserted-by":"publisher"}]},{"name":"UKRI-EPSRC","award":["EP\/Y028805\/1"],"award-info":[{"award-number":["EP\/Y028805\/1"]}]},{"name":"Senior Fellowship awarded by The Kennedy Trust for Rheumatology Research"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,8,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Single-cell RNA sequencing (scRNA-seq) cell annotation techniques rely on the matching of known defining marker genes to a given cell population. However, these methods may lack robustness to dynamic fluctuations in cell marker expression between patients, samples and pathologies. The advent of easy-to-implement predictive technologies, like generative-AI (gen-AI), has facilitated the introduction of computational workflows that improve otherwise inaccurate context-dependent cell type annotation. Here, we introduce CellTypeAI, a streamlined, scalable program developed for tissue context-dependent cell annotation of scRNA-seq datasets using modern gen-AI models, enhanced by retrieval augmented generation methods.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Our implementation builds upon local gen-AI hosting technologies and directly integrates into scRNA-seq analysis pipelines. We show that CellTypeAI provides improved annotation accuracy compared to current conventional annotation methods and nascent cloud-based gen-AI approaches. As CellTypeAI leverages locally-run AI models, it can be applied to sensitive datasets, unlike approaches utilising online gen-AI tools such as ChatGPT, DeepSeek, or Claude. CellTypeAI presents a novel solution for tissue-specific cell type identification, overcoming traditional marker-based limitations via locally-deployed gen-AI models.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>The source code is available at: https:\/\/github.com\/rhdaw\/CellTypeAI.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btag425","type":"journal-article","created":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T20:51:27Z","timestamp":1782852687000},"source":"Crossref","is-referenced-by-count":1,"title":["CellTypeAI: cell annotation for scRNA-seq using local generative-AI"],"prefix":"10.1093","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1258-937X","authenticated-orcid":false,"given":"Rufus H","family":"Daw","sequence":"first","affiliation":[{"name":"Lydia Becker Institute of Immunology and Inflammation, Faculty of Biology, Medicine and Health, The University of Manchester , Manchester, M13 9PL,","place":["United Kingdom"]},{"name":"Division of Immunology, Immunity to Infection and Respiratory Medicine, The University of Manchester , Manchester, M13 9PL,","place":["United Kingdom"]},{"name":"Division of Informatics, Imaging and Data Sciences, The University of Manchester , Manchester, M13 9PL,","place":["United Kingdom"]},{"name":"Medical Research Council Centre of Research Excellence in Exposome Immunology, The University of Manchester , Manchester, M13 9PL,","place":["United Kingdom"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Harry R","family":"Deijnen","sequence":"additional","affiliation":[{"name":"Lydia Becker Institute of Immunology and Inflammation, Faculty of Biology, Medicine and Health, The University of Manchester , Manchester, M13 9PL,","place":["United Kingdom"]},{"name":"Division of Cardiovascular Sciences, The University of Manchester , Manchester, M13 9PL,","place":["United Kingdom"]},{"name":"Geoffrey Jefferson Brain Research Centre, Manchester Academic Health Science Centre, Northern Care Alliance NHS Foundation Trust, The University of Manchester , Manchester, M6 8HD,","place":["United Kingdom"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8196-5565","authenticated-orcid":false,"given":"Magnus","family":"Rattray","sequence":"additional","affiliation":[{"name":"Lydia Becker Institute of Immunology and Inflammation, Faculty of Biology, Medicine and Health, The University of Manchester , Manchester, M13 9PL,","place":["United Kingdom"]},{"name":"Division of Informatics, Imaging and Data Sciences, The University of Manchester , Manchester, M13 9PL,","place":["United Kingdom"]},{"name":"Medical Research Council Centre of Research Excellence in Exposome Immunology, The University of Manchester , Manchester, M13 9PL,","place":["United 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