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Multi-labelled proteins recognition for high-throughput microscopy images using deep convolutional neural networks

BACKGROUND: Proteins are of extremely vital importance in the human body, and no movement or activity can be performed without proteins. Currently, microscopy imaging technologies developed rapidly are employed to observe proteins in various cells and tissues. In addition, due to the complex and cro...

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Autores principales: Zhang, Enze, Zhang, Boheng, Hu, Shaohan, Zhang, Fa, Liu, Zhiyong, Wan, Xiaohua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8207617/
https://www.ncbi.nlm.nih.gov/pubmed/34130623
http://dx.doi.org/10.1186/s12859-021-04196-3
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author Zhang, Enze
Zhang, Boheng
Hu, Shaohan
Zhang, Fa
Liu, Zhiyong
Wan, Xiaohua
author_facet Zhang, Enze
Zhang, Boheng
Hu, Shaohan
Zhang, Fa
Liu, Zhiyong
Wan, Xiaohua
author_sort Zhang, Enze
collection PubMed
description BACKGROUND: Proteins are of extremely vital importance in the human body, and no movement or activity can be performed without proteins. Currently, microscopy imaging technologies developed rapidly are employed to observe proteins in various cells and tissues. In addition, due to the complex and crowded cellular environments as well as various types and sizes of proteins, a considerable number of protein images are generated every day and cannot be classified manually. Therefore, an automatic and accurate method should be designed to properly solve and analyse protein images with mixed patterns. RESULTS: In this paper, we first propose a novel customized architecture with adaptive concatenate pooling and “buffering” layers in the classifier part, which could make the networks more adaptive to training and testing datasets, and develop a novel hard sampler at the end of our network to effectively mine the samples from small classes. Furthermore, a new loss is presented to handle the label imbalance based on the effectiveness of samples. In addition, in our method, several novel and effective optimization strategies are adopted to solve the difficult training-time optimization problem and further increase the accuracy by post-processing. CONCLUSION: Our methods outperformed the SOTA method of multi-labelled protein classification on the HPA dataset, GapNet-PL, by above 2% in the F1 score. Therefore, experimental results based on the test set split from the Human Protein Atlas dataset show that our methods have good performance in automatically classifying multi-class and multi-labelled high-throughput microscopy protein images.
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spelling pubmed-82076172021-06-16 Multi-labelled proteins recognition for high-throughput microscopy images using deep convolutional neural networks Zhang, Enze Zhang, Boheng Hu, Shaohan Zhang, Fa Liu, Zhiyong Wan, Xiaohua BMC Bioinformatics Research BACKGROUND: Proteins are of extremely vital importance in the human body, and no movement or activity can be performed without proteins. Currently, microscopy imaging technologies developed rapidly are employed to observe proteins in various cells and tissues. In addition, due to the complex and crowded cellular environments as well as various types and sizes of proteins, a considerable number of protein images are generated every day and cannot be classified manually. Therefore, an automatic and accurate method should be designed to properly solve and analyse protein images with mixed patterns. RESULTS: In this paper, we first propose a novel customized architecture with adaptive concatenate pooling and “buffering” layers in the classifier part, which could make the networks more adaptive to training and testing datasets, and develop a novel hard sampler at the end of our network to effectively mine the samples from small classes. Furthermore, a new loss is presented to handle the label imbalance based on the effectiveness of samples. In addition, in our method, several novel and effective optimization strategies are adopted to solve the difficult training-time optimization problem and further increase the accuracy by post-processing. CONCLUSION: Our methods outperformed the SOTA method of multi-labelled protein classification on the HPA dataset, GapNet-PL, by above 2% in the F1 score. Therefore, experimental results based on the test set split from the Human Protein Atlas dataset show that our methods have good performance in automatically classifying multi-class and multi-labelled high-throughput microscopy protein images. BioMed Central 2021-06-15 /pmc/articles/PMC8207617/ /pubmed/34130623 http://dx.doi.org/10.1186/s12859-021-04196-3 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Zhang, Enze
Zhang, Boheng
Hu, Shaohan
Zhang, Fa
Liu, Zhiyong
Wan, Xiaohua
Multi-labelled proteins recognition for high-throughput microscopy images using deep convolutional neural networks
title Multi-labelled proteins recognition for high-throughput microscopy images using deep convolutional neural networks
title_full Multi-labelled proteins recognition for high-throughput microscopy images using deep convolutional neural networks
title_fullStr Multi-labelled proteins recognition for high-throughput microscopy images using deep convolutional neural networks
title_full_unstemmed Multi-labelled proteins recognition for high-throughput microscopy images using deep convolutional neural networks
title_short Multi-labelled proteins recognition for high-throughput microscopy images using deep convolutional neural networks
title_sort multi-labelled proteins recognition for high-throughput microscopy images using deep convolutional neural networks
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8207617/
https://www.ncbi.nlm.nih.gov/pubmed/34130623
http://dx.doi.org/10.1186/s12859-021-04196-3
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