Digitally predicting protein localization and manipulating protein activity in fluorescence images using 4D reslicing GAN.

Journal: Bioinformatics (Oxford, England)
PMID: 36373962

Abstract

MOTIVATION: 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.

Authors

  • Yang Jiao
    Department of Neurology, The Second Affiliated Hospital, Harbin Medical University, Harbin, Heilongjiang Province, China.
  • Lingkun Gu
    School of Life Sciences, University of Nevada, Las Vegas, NV 89154, USA.
  • Yingtao Jiang
    Department of Electrical and Computer Engineering, University of Nevada, Las Vegas, NV 89154, USA.
  • Mo Weng
    School of Life Sciences, University of Nevada, Las Vegas, NV 89154, USA.
  • Mei Yang
    Department of Geriatric Cardiology; National Center for Clinical Research of Geriatric Diseases, Xiangya Hospital, Central South University, Changsha 410008, China.

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