Identify DNA-Binding Proteins Through the Extreme Gradient Boosting Algorithm
The exploration of DNA-binding proteins (DBPs) is an important aspect of studying biological life activities. Research on life activities requires the support of scientific research results on DBPs. The decline in many life activities is closely related to DBPs. Generally, the detection method for i...
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| Veröffentlicht in: | Frontiers in genetics Jg. 12; S. 821996 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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28.01.2022
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| ISSN: | 1664-8021, 1664-8021 |
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| Abstract | The exploration of DNA-binding proteins (DBPs) is an important aspect of studying biological life activities. Research on life activities requires the support of scientific research results on DBPs. The decline in many life activities is closely related to DBPs. Generally, the detection method for identifying DBPs is achieved through biochemical experiments. This method is inefficient and requires considerable manpower, material resources and time. At present, several computational approaches have been developed to detect DBPs, among which machine learning (ML) algorithm-based computational techniques have shown excellent performance. In our experiments, our method uses fewer features and simpler recognition methods than other methods and simultaneously obtains satisfactory results. First, we use six feature extraction methods to extract sequence features from the same group of DBPs. Then, this feature information is spliced together, and the data are standardized. Finally, the extreme gradient boosting (XGBoost) model is used to construct an effective predictive model. Compared with other excellent methods, our proposed method has achieved better results. The accuracy achieved by our method is 78.26% for PDB2272 and 85.48% for PDB186. The accuracy of the experimental results achieved by our strategy is similar to that of previous detection methods. |
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| AbstractList | The exploration of DNA-binding proteins (DBPs) is an important aspect of studying biological life activities. Research on life activities requires the support of scientific research results on DBPs. The decline in many life activities is closely related to DBPs. Generally, the detection method for identifying DBPs is achieved through biochemical experiments. This method is inefficient and requires considerable manpower, material resources and time. At present, several computational approaches have been developed to detect DBPs, among which machine learning (ML) algorithm-based computational techniques have shown excellent performance. In our experiments, our method uses fewer features and simpler recognition methods than other methods and simultaneously obtains satisfactory results. First, we use six feature extraction methods to extract sequence features from the same group of DBPs. Then, this feature information is spliced together, and the data are standardized. Finally, the extreme gradient boosting (XGBoost) model is used to construct an effective predictive model. Compared with other excellent methods, our proposed method has achieved better results. The accuracy achieved by our method is 78.26% for PDB2272 and 85.48% for PDB186. The accuracy of the experimental results achieved by our strategy is similar to that of previous detection methods. The exploration of DNA-binding proteins (DBPs) is an important aspect of studying biological life activities. Research on life activities requires the support of scientific research results on DBPs. The decline in many life activities is closely related to DBPs. Generally, the detection method for identifying DBPs is achieved through biochemical experiments. This method is inefficient and requires considerable manpower, material resources and time. At present, several computational approaches have been developed to detect DBPs, among which machine learning (ML) algorithm-based computational techniques have shown excellent performance. In our experiments, our method uses fewer features and simpler recognition methods than other methods and simultaneously obtains satisfactory results. First, we use six feature extraction methods to extract sequence features from the same group of DBPs. Then, this feature information is spliced together, and the data are standardized. Finally, the extreme gradient boosting (XGBoost) model is used to construct an effective predictive model. Compared with other excellent methods, our proposed method has achieved better results. The accuracy achieved by our method is 78.26% for PDB2272 and 85.48% for PDB186. The accuracy of the experimental results achieved by our strategy is similar to that of previous detection methods.The exploration of DNA-binding proteins (DBPs) is an important aspect of studying biological life activities. Research on life activities requires the support of scientific research results on DBPs. The decline in many life activities is closely related to DBPs. Generally, the detection method for identifying DBPs is achieved through biochemical experiments. This method is inefficient and requires considerable manpower, material resources and time. At present, several computational approaches have been developed to detect DBPs, among which machine learning (ML) algorithm-based computational techniques have shown excellent performance. In our experiments, our method uses fewer features and simpler recognition methods than other methods and simultaneously obtains satisfactory results. First, we use six feature extraction methods to extract sequence features from the same group of DBPs. Then, this feature information is spliced together, and the data are standardized. Finally, the extreme gradient boosting (XGBoost) model is used to construct an effective predictive model. Compared with other excellent methods, our proposed method has achieved better results. The accuracy achieved by our method is 78.26% for PDB2272 and 85.48% for PDB186. The accuracy of the experimental results achieved by our strategy is similar to that of previous detection methods. |
| Author | Yang, Wen Zhao, Yuming Zhao, Ziye Zhai, Yixiao Liang, Yingjian |
| AuthorAffiliation | 1 College of Information and Computer Engineering, Northeast Forestry University , Harbin , China 3 Department of Obstetrics and Gynecology, The First Affiliated Hospital of Harbin Medical University , Harbin , China 2 International Medical Center, Shenzhen University General Hospital , Shenzhen , China |
| AuthorAffiliation_xml | – name: 2 International Medical Center, Shenzhen University General Hospital , Shenzhen , China – name: 3 Department of Obstetrics and Gynecology, The First Affiliated Hospital of Harbin Medical University , Harbin , China – name: 1 College of Information and Computer Engineering, Northeast Forestry University , Harbin , China |
| Author_xml | – sequence: 1 givenname: Ziye surname: Zhao fullname: Zhao, Ziye – sequence: 2 givenname: Wen surname: Yang fullname: Yang, Wen – sequence: 3 givenname: Yixiao surname: Zhai fullname: Zhai, Yixiao – sequence: 4 givenname: Yingjian surname: Liang fullname: Liang, Yingjian – sequence: 5 givenname: Yuming surname: Zhao fullname: Zhao, Yuming |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35154264$$D View this record in MEDLINE/PubMed |
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| Keywords | DNA-binding protein prediction dimensionality reduction machine learning feature extraction XGBoost model |
| Language | English |
| License | Copyright © 2022 Zhao, Yang, Zhai, Liang and Zhao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 ObjectType-Review-3 content type line 23 Junwei Luo, Henan Polytechnic University, China Edited by: Juan Wang, Inner Mongolia University, China This article was submitted to Statistical Genetics and Methodology, a section of the journal Frontiers in Genetics These authors have contributed equally to this work Reviewed by: Wei Lan, Guangxi University, China |
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| Title | Identify DNA-Binding Proteins Through the Extreme Gradient Boosting Algorithm |
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