ECMPride: prediction of human extracellular matrix proteins based on the ideal dataset using hybrid features with domain evidence
Extracellular matrix (ECM) proteins play an essential role in various biological processes in multicellular organisms, and their abnormal regulation can lead to many diseases. For large-scale ECM protein identification, especially through proteomic-based techniques, a theoretical reference database...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2020-04-01
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| Series: | PeerJ |
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| Online Access: | https://peerj.com/articles/9066.pdf |
| _version_ | 1797418265365970944 |
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| author | Binghui Liu Ling Leng Xuer Sun Yunfang Wang Jie Ma Yunping Zhu |
| author_facet | Binghui Liu Ling Leng Xuer Sun Yunfang Wang Jie Ma Yunping Zhu |
| author_sort | Binghui Liu |
| collection | DOAJ |
| description | Extracellular matrix (ECM) proteins play an essential role in various biological processes in multicellular organisms, and their abnormal regulation can lead to many diseases. For large-scale ECM protein identification, especially through proteomic-based techniques, a theoretical reference database of ECM proteins is required. In this study, based on the experimentally verified ECM datasets and by the integration of protein domain features and a machine learning model, we developed ECMPride, a flexible and scalable tool for predicting ECM proteins. ECMPride achieved excellent performance in predicting ECM proteins, with appropriate balanced accuracy and sensitivity, and the performance of ECMPride was shown to be superior to the previously developed tool. A new theoretical dataset of human ECM components was also established by applying ECMPride to all human entries in the SwissProt database, containing a significant number of putative ECM proteins as well as the abundant biological annotations. This dataset might serve as a valuable reference resource for ECM protein identification. |
| first_indexed | 2024-03-09T06:30:09Z |
| format | Article |
| id | doaj.art-951e8a408e764f71a79cac7b3beece59 |
| institution | Directory Open Access Journal |
| issn | 2167-8359 |
| language | English |
| last_indexed | 2024-03-09T06:30:09Z |
| publishDate | 2020-04-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ |
| spelling | doaj.art-951e8a408e764f71a79cac7b3beece592023-12-03T11:06:06ZengPeerJ Inc.PeerJ2167-83592020-04-018e906610.7717/peerj.9066ECMPride: prediction of human extracellular matrix proteins based on the ideal dataset using hybrid features with domain evidenceBinghui Liu0Ling Leng1Xuer Sun2Yunfang Wang3Jie Ma4Yunping Zhu5State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Life Omics, Beijing, ChinaDepartment of Central Laboratory, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, ChinaTissue Engineering Lab, Institute of Health Service and Transfusion Medicine, Beijing, ChinaTissue Engineering Lab, Institute of Health Service and Transfusion Medicine, Beijing, ChinaState Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Life Omics, Beijing, ChinaState Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Life Omics, Beijing, ChinaExtracellular matrix (ECM) proteins play an essential role in various biological processes in multicellular organisms, and their abnormal regulation can lead to many diseases. For large-scale ECM protein identification, especially through proteomic-based techniques, a theoretical reference database of ECM proteins is required. In this study, based on the experimentally verified ECM datasets and by the integration of protein domain features and a machine learning model, we developed ECMPride, a flexible and scalable tool for predicting ECM proteins. ECMPride achieved excellent performance in predicting ECM proteins, with appropriate balanced accuracy and sensitivity, and the performance of ECMPride was shown to be superior to the previously developed tool. A new theoretical dataset of human ECM components was also established by applying ECMPride to all human entries in the SwissProt database, containing a significant number of putative ECM proteins as well as the abundant biological annotations. This dataset might serve as a valuable reference resource for ECM protein identification.https://peerj.com/articles/9066.pdfExtracellular matrix proteinsProteomicsPrediction toolRandom forestUnder-sampling ensemble method |
| spellingShingle | Binghui Liu Ling Leng Xuer Sun Yunfang Wang Jie Ma Yunping Zhu ECMPride: prediction of human extracellular matrix proteins based on the ideal dataset using hybrid features with domain evidence PeerJ Extracellular matrix proteins Proteomics Prediction tool Random forest Under-sampling ensemble method |
| title | ECMPride: prediction of human extracellular matrix proteins based on the ideal dataset using hybrid features with domain evidence |
| title_full | ECMPride: prediction of human extracellular matrix proteins based on the ideal dataset using hybrid features with domain evidence |
| title_fullStr | ECMPride: prediction of human extracellular matrix proteins based on the ideal dataset using hybrid features with domain evidence |
| title_full_unstemmed | ECMPride: prediction of human extracellular matrix proteins based on the ideal dataset using hybrid features with domain evidence |
| title_short | ECMPride: prediction of human extracellular matrix proteins based on the ideal dataset using hybrid features with domain evidence |
| title_sort | ecmpride prediction of human extracellular matrix proteins based on the ideal dataset using hybrid features with domain evidence |
| topic | Extracellular matrix proteins Proteomics Prediction tool Random forest Under-sampling ensemble method |
| url | https://peerj.com/articles/9066.pdf |
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