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https://doi.org/10.17559/TV-20190730093945

An Improved Prediction Model for Zinc-Binding Sites in Proteins Based on Bayesian Method

Hui Li ; (1) School of Software Engineering, Jinling Institute of Technology, Jiangsu 211169, China / (2) Center for Intelligent Computer Human Interaction, Nanjing Institute of Big Data, Jiangsu, 211169 China / (3) College of Computer Science and Technology, Nanj
Dechang Pi   ORCID icon orcid.org/0000-0002-6593-4563 ; College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Jiangsu, 211169 China
Chuanming Chen ; College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Jiangsu, 211169 China

Puni tekst: engleski, pdf (464 KB) str. 1422-1426 preuzimanja: 43* citiraj
APA 6th Edition
Li, H., Pi, D. i Chen, C. (2019). An Improved Prediction Model for Zinc-Binding Sites in Proteins Based on Bayesian Method. Tehnički vjesnik, 26 (5), 1422-1426. https://doi.org/10.17559/TV-20190730093945
MLA 8th Edition
Li, Hui, et al. "An Improved Prediction Model for Zinc-Binding Sites in Proteins Based on Bayesian Method." Tehnički vjesnik, vol. 26, br. 5, 2019, str. 1422-1426. https://doi.org/10.17559/TV-20190730093945. Citirano 15.11.2019.
Chicago 17th Edition
Li, Hui, Dechang Pi i Chuanming Chen. "An Improved Prediction Model for Zinc-Binding Sites in Proteins Based on Bayesian Method." Tehnički vjesnik 26, br. 5 (2019): 1422-1426. https://doi.org/10.17559/TV-20190730093945
Harvard
Li, H., Pi, D., i Chen, C. (2019). 'An Improved Prediction Model for Zinc-Binding Sites in Proteins Based on Bayesian Method', Tehnički vjesnik, 26(5), str. 1422-1426. https://doi.org/10.17559/TV-20190730093945
Vancouver
Li H, Pi D, Chen C. An Improved Prediction Model for Zinc-Binding Sites in Proteins Based on Bayesian Method. Tehnički vjesnik [Internet]. 2019 [pristupljeno 15.11.2019.];26(5):1422-1426. https://doi.org/10.17559/TV-20190730093945
IEEE
H. Li, D. Pi i C. Chen, "An Improved Prediction Model for Zinc-Binding Sites in Proteins Based on Bayesian Method", Tehnički vjesnik, vol.26, br. 5, str. 1422-1426, 2019. [Online]. https://doi.org/10.17559/TV-20190730093945

Sažetak
The zinc ion is the second richest metal ion in organisms. The proteins binding to zinc ions have important biological functions. However, few scholars have integrated the existing tools to predict the zinc-binding sites in proteins. To make up for this gap, this paper combines three well-known prediction tools into an improved model called IBayes_Zinc to predict the zinc-binding sites, and utilizes the advantages of the Bayesian method in handling incomplete or partial missing data. Specifically, the prediction scores of three existing sequence-based prediction tools were adopted, and the missing values were padded, forming an integrated classification tool. Then, the probabilities of positive and negative samples were computed and categorized as the class with higher probabilities. Experiments were conducted on a non-redundant training dataset and an independent testing dataset. The results show that our method surpassed the other three methods by nearly 5–13% in Matthew correlation coefficient (MCC) and outperformed the latter in recall and precision. The research findings promote the detection of zinc-binding sites in protein sequence and the identification of metalloprotein functions.

Ključne riječi
Bayesian; missing; protein prediction; zinc-binding sites

Hrčak ID: 226039

URI
https://hrcak.srce.hr/226039

Posjeta: 82 *