A Convolutional Neural Network-Based Framework for Classification of Protein Localization Using Confocal Microscopy Images
<p>Understanding protein subcellular localization is vital and indispensable in proteomics research. Molecular biology and computer science developments have enabled the use of computational approaches to identify proteins in cells. An excellent method for locating proteins is confocal microsc...
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| مؤلفون آخرون: | , , , , , |
| منشور في: |
2022
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| _version_ | 1864513561026363392 |
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| author | Sonam Aggarwal (16904763) |
| author2 | Sheifali Gupta (16904766) Ramani Kannan (13463790) Rakesh Ahuja (16904769) Deepali Gupta (798607) Sapna Juneja (16904772) Samir Brahim Belhaouari (9427347) |
| author2_role | author author author author author author |
| author_facet | Sonam Aggarwal (16904763) Sheifali Gupta (16904766) Ramani Kannan (13463790) Rakesh Ahuja (16904769) Deepali Gupta (798607) Sapna Juneja (16904772) Samir Brahim Belhaouari (9427347) |
| author_role | author |
| dc.creator.none.fl_str_mv | Sonam Aggarwal (16904763) Sheifali Gupta (16904766) Ramani Kannan (13463790) Rakesh Ahuja (16904769) Deepali Gupta (798607) Sapna Juneja (16904772) Samir Brahim Belhaouari (9427347) |
| dc.date.none.fl_str_mv | 2022-08-08T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2022.3197189 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/A_Convolutional_Neural_Network-Based_Framework_for_Classification_of_Protein_Localization_Using_Confocal_Microscopy_Images/24056382 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biological sciences Bioinformatics and computational biology Information and computing sciences Computer vision and multimedia computation Machine learning Proteins Location awareness Protein engineering Feature extraction Microscopy Convolutional neural networks Computer architecture Biomedical image analysis Deep learning Human protein atlas image classification Protein subcellular localization prediction |
| dc.title.none.fl_str_mv | A Convolutional Neural Network-Based Framework for Classification of Protein Localization Using Confocal Microscopy Images |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>Understanding protein subcellular localization is vital and indispensable in proteomics research. Molecular biology and computer science developments have enabled the use of computational approaches to identify proteins in cells. An excellent method for locating proteins is confocal microscopy, used by the Human Protein Atlas (HPA). By categorizing human proteins, it can assist researchers in better comprehending human pathophysiology and assist doctors in automating medical image interpretation. Human protein Atlas comprises millions of images annotated with single or multiple labels. However, only a few methods for automated prediction of protein localization have been developed, and they mostly concentrate on single-label classification. Therefore, a recognition system for multi-label classification of HPA with acceptable performance should be developed. Hence, this study aims to develop a deep learning-based system for the multi-label classification of HPA. Specifically, two architectures have been proposed in this work for automatically extracting features from the images and predicting the localization of the proteins in 28 subcellular compartments. First, a convolutional neural network has been proposed, which has been trained from scratch and second an ensemble-based model using transfer learning architectures has been proposed. The results demonstrate that both models are effective in classifying proteins according to their location in the major cellular organelles. Yet, in this study, the proposed convolutional network outperforms the ensemble model in classification of images with multiple simultaneous protein localizations. Three performance metrics standards—recall, accuracy, and f1-score—were used to assess the models. The proposed convolutional neural network beats the ensemble model by achieving recall of 0.75, precision of 0.75 and f1-score of 0.74.</p><h2>Other Information</h2><p>Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/legalcode" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2022.3197189" target="_blank">https://dx.doi.org/10.1109/access.2022.3197189</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_bea192520c489625049b83a6fdee9df9 |
| identifier_str_mv | 10.1109/access.2022.3197189 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24056382 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | A Convolutional Neural Network-Based Framework for Classification of Protein Localization Using Confocal Microscopy ImagesSonam Aggarwal (16904763)Sheifali Gupta (16904766)Ramani Kannan (13463790)Rakesh Ahuja (16904769)Deepali Gupta (798607)Sapna Juneja (16904772)Samir Brahim Belhaouari (9427347)Biological sciencesBioinformatics and computational biologyInformation and computing sciencesComputer vision and multimedia computationMachine learningProteinsLocation awarenessProtein engineeringFeature extractionMicroscopyConvolutional neural networksComputer architectureBiomedical image analysisDeep learningHuman protein atlas image classificationProtein subcellular localization prediction<p>Understanding protein subcellular localization is vital and indispensable in proteomics research. Molecular biology and computer science developments have enabled the use of computational approaches to identify proteins in cells. An excellent method for locating proteins is confocal microscopy, used by the Human Protein Atlas (HPA). By categorizing human proteins, it can assist researchers in better comprehending human pathophysiology and assist doctors in automating medical image interpretation. Human protein Atlas comprises millions of images annotated with single or multiple labels. However, only a few methods for automated prediction of protein localization have been developed, and they mostly concentrate on single-label classification. Therefore, a recognition system for multi-label classification of HPA with acceptable performance should be developed. Hence, this study aims to develop a deep learning-based system for the multi-label classification of HPA. Specifically, two architectures have been proposed in this work for automatically extracting features from the images and predicting the localization of the proteins in 28 subcellular compartments. First, a convolutional neural network has been proposed, which has been trained from scratch and second an ensemble-based model using transfer learning architectures has been proposed. The results demonstrate that both models are effective in classifying proteins according to their location in the major cellular organelles. Yet, in this study, the proposed convolutional network outperforms the ensemble model in classification of images with multiple simultaneous protein localizations. Three performance metrics standards—recall, accuracy, and f1-score—were used to assess the models. The proposed convolutional neural network beats the ensemble model by achieving recall of 0.75, precision of 0.75 and f1-score of 0.74.</p><h2>Other Information</h2><p>Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/legalcode" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2022.3197189" target="_blank">https://dx.doi.org/10.1109/access.2022.3197189</a></p>2022-08-08T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2022.3197189https://figshare.com/articles/journal_contribution/A_Convolutional_Neural_Network-Based_Framework_for_Classification_of_Protein_Localization_Using_Confocal_Microscopy_Images/24056382CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/240563822022-08-08T00:00:00Z |
| spellingShingle | A Convolutional Neural Network-Based Framework for Classification of Protein Localization Using Confocal Microscopy Images Sonam Aggarwal (16904763) Biological sciences Bioinformatics and computational biology Information and computing sciences Computer vision and multimedia computation Machine learning Proteins Location awareness Protein engineering Feature extraction Microscopy Convolutional neural networks Computer architecture Biomedical image analysis Deep learning Human protein atlas image classification Protein subcellular localization prediction |
| status_str | publishedVersion |
| title | A Convolutional Neural Network-Based Framework for Classification of Protein Localization Using Confocal Microscopy Images |
| title_full | A Convolutional Neural Network-Based Framework for Classification of Protein Localization Using Confocal Microscopy Images |
| title_fullStr | A Convolutional Neural Network-Based Framework for Classification of Protein Localization Using Confocal Microscopy Images |
| title_full_unstemmed | A Convolutional Neural Network-Based Framework for Classification of Protein Localization Using Confocal Microscopy Images |
| title_short | A Convolutional Neural Network-Based Framework for Classification of Protein Localization Using Confocal Microscopy Images |
| title_sort | A Convolutional Neural Network-Based Framework for Classification of Protein Localization Using Confocal Microscopy Images |
| topic | Biological sciences Bioinformatics and computational biology Information and computing sciences Computer vision and multimedia computation Machine learning Proteins Location awareness Protein engineering Feature extraction Microscopy Convolutional neural networks Computer architecture Biomedical image analysis Deep learning Human protein atlas image classification Protein subcellular localization prediction |