DeepETC: a deep convolutional neural network architecture for investigating and classifying electron transport chain's complexes
An electron transport chain is a series of protein complexes embedded in the transport protein, which is an important process to transfer electrons and other macromolecules throughout the cell. It is the primary process to extract energy via redox reactions in the case of oxidation of sugars in cell...
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| Format: | Journal Article |
| Language: | English |
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2022
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| Online Access: | https://hdl.handle.net/10356/160971 |
| _version_ | 1826115599480127488 |
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| author | Le, Nguyen Quoc Khanh Ho, Quang-Thai Yapp, Edward Kien Yee Ou, Yu-Yen Yeh, Hui-Yuan |
| author2 | School of Humanities |
| author_facet | School of Humanities Le, Nguyen Quoc Khanh Ho, Quang-Thai Yapp, Edward Kien Yee Ou, Yu-Yen Yeh, Hui-Yuan |
| author_sort | Le, Nguyen Quoc Khanh |
| collection | NTU |
| description | An electron transport chain is a series of protein complexes embedded in the transport protein, which is an important process to transfer electrons and other macromolecules throughout the cell. It is the primary process to extract energy via redox reactions in the case of oxidation of sugars in cellular respiration. According to the molecular functions, the components of the electron transport chain could be formed with five complexes and with several different electron carriers. The functional loss of a specific molecular function in electron transport chain has been implicated in a variety of human diseases such as diabetes, neurodegenerative disorders, Parkinson, and Alzheimer's disease. Therefore, creating a precise model to identify its functions is pertinent to the understanding of human diseases and designing of drug targets. Previous bioinformatics studies have almost exclusively focused on the electron transport proteins without information on the five complexes. Here we present DeepETC, a deep learning model that uses a two-dimensional convolutional neural network and position-specific scoring matrices profiles to classify electron transport proteins into the five complexes. DeepETC can classify the electron transporters with the independent test accuracy of 99.6%, 99.7%, 99.7%, 99.1% and 99.8% for complex I, II, III, IV, and V, respectively. Our performance results are significantly more accurate than the state-of-the-art traditional neural networks in all typical measurement metrics. Throughout the proposed study, we provide an effective tool for investigating electron transport proteins and our achievement could promote the use of deep learning in bioinformatics and computational biology. DeepETC can be freely accessible via http://www.biologydeep.com/deepetc/. |
| first_indexed | 2024-10-01T03:57:50Z |
| format | Journal Article |
| id | ntu-10356/160971 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:57:50Z |
| publishDate | 2022 |
| record_format | dspace |
| spelling | ntu-10356/1609712022-08-10T01:51:46Z DeepETC: a deep convolutional neural network architecture for investigating and classifying electron transport chain's complexes Le, Nguyen Quoc Khanh Ho, Quang-Thai Yapp, Edward Kien Yee Ou, Yu-Yen Yeh, Hui-Yuan School of Humanities Medical Humanities Research Cluster Science::Medicine Deep Learning Cellular Respiration An electron transport chain is a series of protein complexes embedded in the transport protein, which is an important process to transfer electrons and other macromolecules throughout the cell. It is the primary process to extract energy via redox reactions in the case of oxidation of sugars in cellular respiration. According to the molecular functions, the components of the electron transport chain could be formed with five complexes and with several different electron carriers. The functional loss of a specific molecular function in electron transport chain has been implicated in a variety of human diseases such as diabetes, neurodegenerative disorders, Parkinson, and Alzheimer's disease. Therefore, creating a precise model to identify its functions is pertinent to the understanding of human diseases and designing of drug targets. Previous bioinformatics studies have almost exclusively focused on the electron transport proteins without information on the five complexes. Here we present DeepETC, a deep learning model that uses a two-dimensional convolutional neural network and position-specific scoring matrices profiles to classify electron transport proteins into the five complexes. DeepETC can classify the electron transporters with the independent test accuracy of 99.6%, 99.7%, 99.7%, 99.1% and 99.8% for complex I, II, III, IV, and V, respectively. Our performance results are significantly more accurate than the state-of-the-art traditional neural networks in all typical measurement metrics. Throughout the proposed study, we provide an effective tool for investigating electron transport proteins and our achievement could promote the use of deep learning in bioinformatics and computational biology. DeepETC can be freely accessible via http://www.biologydeep.com/deepetc/. Nanyang Technological University This research is partially supported by the Nanyang Technological University Start-Up Grant and the Ministry of Science and Technology, Taiwan, R.O.C. under Grant no. MOST 106-2221-E-155-068. 2022-08-10T01:51:46Z 2022-08-10T01:51:46Z 2020 Journal Article Le, N. Q. K., Ho, Q., Yapp, E. K. Y., Ou, Y. & Yeh, H. (2020). DeepETC: a deep convolutional neural network architecture for investigating and classifying electron transport chain's complexes. Neurocomputing, 375, 71-79. https://dx.doi.org/10.1016/j.neucom.2019.09.070 0925-2312 https://hdl.handle.net/10356/160971 10.1016/j.neucom.2019.09.070 2-s2.0-85073072519 375 71 79 en Neurocomputing © 2019 Elsevier B.V. All rights reserved. |
| spellingShingle | Science::Medicine Deep Learning Cellular Respiration Le, Nguyen Quoc Khanh Ho, Quang-Thai Yapp, Edward Kien Yee Ou, Yu-Yen Yeh, Hui-Yuan DeepETC: a deep convolutional neural network architecture for investigating and classifying electron transport chain's complexes |
| title | DeepETC: a deep convolutional neural network architecture for investigating and classifying electron transport chain's complexes |
| title_full | DeepETC: a deep convolutional neural network architecture for investigating and classifying electron transport chain's complexes |
| title_fullStr | DeepETC: a deep convolutional neural network architecture for investigating and classifying electron transport chain's complexes |
| title_full_unstemmed | DeepETC: a deep convolutional neural network architecture for investigating and classifying electron transport chain's complexes |
| title_short | DeepETC: a deep convolutional neural network architecture for investigating and classifying electron transport chain's complexes |
| title_sort | deepetc a deep convolutional neural network architecture for investigating and classifying electron transport chain s complexes |
| topic | Science::Medicine Deep Learning Cellular Respiration |
| url | https://hdl.handle.net/10356/160971 |
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