Ulicna, Kristina;
Ho, Laure TL;
Soelistyo, Christopher J;
Day, Nathan J;
Lowe, Alan R;
(2022)
Convolutional Neural Networks for Classifying Chromatin Morphology in Live-Cell Imaging.
Methods in Molecular Biology
, 2476
pp. 17-30.
10.1007/978-1-0716-2221-6_3.
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Abstract
Chromatin is highly structured, and changes in its organization are essential in many cellular processes, including cell division. Recently, advances in machine learning have enabled researchers to automatically classify chromatin morphology in fluorescence microscopy images. In this protocol, we develop user-friendly tools to perform this task. We provide an open-source annotation tool, and a cloud-based computational framework to train and utilize a convolutional neural network to automatically classify chromatin morphology. Using cloud compute enables users without significant resources or computational experience to use a machine learning approach to analyze their own microscopy data.
| Type: | Article |
|---|---|
| Title: | Convolutional Neural Networks for Classifying Chromatin Morphology in Live-Cell Imaging |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/978-1-0716-2221-6_3 |
| Publisher version: | https://doi.org/10.1007/978-1-0716-2221-6_3 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Machine learning, Live-cell imaging, Cell cycle, Image analysis, Computational biology |
| UCL classification: | UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Div of Biosciences > Structural and Molecular Biology UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Div of Biosciences |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10149543 |
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