Towards 'end-to-end' analysis and understanding of biological timecourse data Editorial


Authors: Jena, S. G.; Goglia, A. G.; Engelhardt, B. E.
Title: Towards 'end-to-end' analysis and understanding of biological timecourse data
Abstract: Petabytes of increasingly complex and multidimensional live cell and tissue imaging data are generated every year. These videos hold large promise for understanding biology at a deep and fundamental level, as they capture single-cell and multicellular events occurring over time and space. However, the current modalities for analysis and mining of these data are scattered and user-specific, preventing more unified analyses from being performed over different datasets and obscuring possible scientific insights. Here, we propose a unified pipeline for storage, segmentation, analysis, and statistical parametrization of live cell imaging datasets. © 2022 The Author(s).
Keywords: machine learning; pipeline; article; live cell imaging
Journal Title: Biochemical Journal
Volume: 479
Issue: 11
ISSN: 0264-6021
Publisher: Portland Press Ltd  
Date Published: 2022-06-01
Start Page: 1257
End Page: 1263
Language: English
DOI: 10.1042/bcj20220053
PUBMED: 35713413
PROVIDER: scopus
PMCID: PMC9246344
DOI/URL:
Notes: Article -- Export Date: 1 August 2022 -- Source: Scopus
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  1. Alexander George Goglia
    14 Goglia