Document Type
Article
Publication Date
11-14-2022
Publication Title
Bioinformatics
Volume
39
Issue
1
First page number:
1
Last page number:
10
Abstract
While multi-channel fluorescence microscopy is a vital imaging method in biological studies, the number of channels that can be imaged simultaneously is limited by technical and hardware limitations such as emission spectra cross-talk. One solution is using deep neural networks to model the localization relationship between two proteins so that the localization of one protein can be digitally predicted. Furthermore, the input and predicted localization implicitly reflect the modeled relationship. Accordingly, observing the response of the prediction via manipulating input localization could provide an informative way to analyze the modeled relationships between the input and the predicted proteins.
Controlled Subject
Proteins--Analysis; Fluorescence microscopy
Disciplines
Biomedical | Biotechnology
File Format
File Size
2900 KB
Language
English
Rights
IN COPYRIGHT. For more information about this rights statement, please visit http://rightsstatements.org/vocab/InC/1.0/
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Repository Citation
Jiao, Y.,
Gu, L.,
Jiang, Y.,
Weng, M.,
Yang, M.
(2022).
Digitally Predicting Protein Localization and Manipulating Protein Activity in Fluorescence Images Using 4D Reslicing GAN.
Bioinformatics, 39(1),
1-10.
http://dx.doi.org/10.1093/bioinformatics/btac719