CLANet: A comprehensive framework for cross-batch cell line identification using brightfield images.

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作者： Lei,Tong ^[1] ; Adam,Corrigan ^[2] ; Navin Rathna,Kumar ^[3] ; Kerry,Hallbrook ^[3] ; Jonathan,Orme ^[4] ; Yinhai,Wang ^[5] ; Huiyu,Zhou ^[6]

作者单位： School of Computing and Mathematical Sciences, University of Leicester, Leicester, UK; Data Sciences and Quantitative Biology, Discovery Sciences, AstraZeneca R&D, Cambridge, UK. ^[1] Data Sciences and Quantitative Biology, Discovery Sciences, AstraZeneca R&D, Cambridge, UK. ^[2] UK Cell Culture and Banking, Discovery Sciences, AstraZeneca R&D, Alderley Park, UK. ^[3] UK Cell Culture and Banking, Discovery Sciences, AstraZeneca R&D, Cambridge, UK. ^[4] Data Sciences and Quantitative Biology, Discovery Sciences, AstraZeneca R&D, Cambridge, UK. Electronic address: yinhai.wang@astrazeneca.com. ^[5] School of Computing and Mathematical Sciences, University of Leicester, Leicester, UK. Electronic address: hz143@leicester.ac.uk. ^[6]

关键词 Batch effect Brightfield image analysis Cell line authentication Multiple instance learning

主题词人类(Humans);胰腺(Pancreas);时间因素(Time Factors)

DOI 10.1016/j.media.2024.103123

PMID 38430651

发布时间 2024-04-16

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