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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Vydané v:Frontiers in genetics Ročník 12; s. 821996
Hlavní autori: Zhao, Ziye, Yang, Wen, Zhai, Yixiao, Liang, Yingjian, Zhao, Yuming
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Switzerland Frontiers Media S.A 28.01.2022
Predmet:
dimensionality reduction
DNA-binding protein prediction
feature extraction
Genetics
machine learning
XGBoost model
DNA-binding protein prediction
dimensionality reduction
machine learning
feature extraction
XGBoost model
ISSN:1664-8021, 1664-8021
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Shrnutí: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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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
ISSN:1664-8021
1664-8021
DOI:10.3389/fgene.2021.821996