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Article

Convolutional Neural Network-Based Artificial Intelligence for Classification of Protein Localization Patterns

1
Faculty of Medicine and Health Technology, Tampere University, FI-33520 Tampere, Finland
2
Department of Applied Physics, University of Eastern Finland, FI-70211 Kuopio, Finland
3
Institute of Biomedicine, University of Eastern Finland, FI-70211 Kuopio, Finland
4
Institute of Biomedicine, University of Turku, FI-20014 Turku, Finland
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Ryuji Hamamoto
Biomolecules 2021, 11(2), 264; https://doi.org/10.3390/biom11020264
Received: 11 December 2020 / Revised: 15 January 2021 / Accepted: 19 January 2021 / Published: 11 February 2021
Identifying localization of proteins and their specific subpopulations associated with certain cellular compartments is crucial for understanding protein function and interactions with other macromolecules. Fluorescence microscopy is a powerful method to assess protein localizations, with increasing demand of automated high throughput analysis methods to supplement the technical advancements in high throughput imaging. Here, we study the applicability of deep neural network-based artificial intelligence in classification of protein localization in 13 cellular subcompartments. We use deep learning-based on convolutional neural network and fully convolutional network with similar architectures for the classification task, aiming at achieving accurate classification, but importantly, also comparison of the networks. Our results show that both types of convolutional neural networks perform well in protein localization classification tasks for major cellular organelles. Yet, in this study, the fully convolutional network outperforms the convolutional neural network in classification of images with multiple simultaneous protein localizations. We find that the fully convolutional network, using output visualizing the identified localizations, is a very useful tool for systematic protein localization assessment. View Full-Text
Keywords: protein localization; artificial intelligence; convolutional neural networks; deep learning; phenotyping; classification; fluorescence microscopy; cellular organelles protein localization; artificial intelligence; convolutional neural networks; deep learning; phenotyping; classification; fluorescence microscopy; cellular organelles
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MDPI and ACS Style

Liimatainen, K.; Huttunen, R.; Latonen, L.; Ruusuvuori, P. Convolutional Neural Network-Based Artificial Intelligence for Classification of Protein Localization Patterns. Biomolecules 2021, 11, 264. https://doi.org/10.3390/biom11020264

AMA Style

Liimatainen K, Huttunen R, Latonen L, Ruusuvuori P. Convolutional Neural Network-Based Artificial Intelligence for Classification of Protein Localization Patterns. Biomolecules. 2021; 11(2):264. https://doi.org/10.3390/biom11020264

Chicago/Turabian Style

Liimatainen, Kaisa, Riku Huttunen, Leena Latonen, and Pekka Ruusuvuori. 2021. "Convolutional Neural Network-Based Artificial Intelligence for Classification of Protein Localization Patterns" Biomolecules 11, no. 2: 264. https://doi.org/10.3390/biom11020264

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