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Deep localization of subcellular protein structures from fluorescence microscopy images

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Abstract

Accurate localization of proteins from fluorescence microscopy images is challenging due to the inter-class similarities and intra-class disparities introducing grave concerns in addressing multi-class classification problems. Conventional machine learning-based image prediction pipelines rely heavily on pre-processing such as normalization and segmentation followed by handcrafted feature extraction to identify useful, informative, and application-specific features. Here, we demonstrate that deep learning-based pipelines can effectively classify protein images from different datasets. We propose an end-to-end Protein Localization Convolutional Neural Network (PLCNN) that classifies protein images more accurately and reliably. PLCNN processes raw imagery without involving any pre-processing steps and produces outputs without any customization or parameter adjustment for a particular dataset. Experimental analysis is performed on five benchmark datasets. PLCNN consistently outperformed the existing state-of-the-art approaches from traditional machine learning and deep architectures. This study highlights the importance of deep learning for the analysis of fluorescence microscopy protein imagery. The proposed deep pipeline can better guide drug designing procedures in the pharmaceutical industry and open new avenues for researchers in computational biology and bioinformatics.

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Availability of data and material

Publicly available datasets are used in this study.

Notes

  1. Code available at https://github.com/saeed-anwar/PLCNN.

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Funding

This research did not receive any specific funding.

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Authors and Affiliations

  1. College of Computing and Informatics, Saudi Electronic University, Riyadh, Saudi Arabia

    Muhammad Tahir & Abdul Wahab Muzaffar

  2. Data61, The Commonwealth Scientific and Industrial Research Organisation, Canberra, Australia

    Saeed Anwar

  3. Department of Computer Science and Engineering, The Australian National University, Canberra, Australia

    Saeed Anwar

  4. School of Computer Science, University of Technology Sydney, Ultimo, Australia

    Saeed Anwar

  5. Computer Science and Software Engineering, The University of Western Australia, Crawley, Australia

    Ajmal Mian

Authors
  1. Muhammad Tahir
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  2. Saeed Anwar
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  4. Abdul Wahab Muzaffar
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Correspondence to Muhammad Tahir.

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Since no experiments are performed on humans or animals (dead or alive) in this research, therefore, Ethical approval is not required.

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Code available at https://github.com/saeed-anwar/PLCNN

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Tahir, M., Anwar, S., Mian, A. et al. Deep localization of subcellular protein structures from fluorescence microscopy images. Neural Comput & Applic 34, 5701–5714 (2022). https://doi.org/10.1007/s00521-021-06715-y

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