IMR Press / FBL / Volume 27 / Issue 1 / DOI: 10.31083/j.fbl2701012
Open Access Original Research
ET-MSF: a model stacking framework to identify electron transport proteins
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1 Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, 610054 Chengdu, Sichuan, China
2 Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, 324000 Quzhou, Zhejiang, China
3 Department of Oncology Radiology, Beidahuang Industry Group General Hospital, 150000 Harbin, Heilongjiang, China
4 Department of Nephrology, The Affiliated Wuxi People's Hospital of Nanjing Medical University, 214023 Wuxi, Jiangsu, China

Academic Editor: Graham Pawelec

Front. Biosci. (Landmark Ed) 2022 , 27(1), 1; https://doi.org/10.31083/j.fbl2701012
Submitted: 18 October 2021 | Revised: 26 November 2021 | Accepted: 9 December 2021 | Published: 11 January 2022
(This article belongs to the Special Issue Computational biomarker detection and analysis)
Copyright: © 2022 The Author(s). Published by IMR Press.
This is an open access article under the CC BY 4.0 license.
Abstract

Introduction: The electron transport chain is closely related to cellular respiration and has been implicated in various human diseases. However, the traditional “wet” experimental method is time consuming. Therefore, it is key to identify electron transport proteins by computational methods. Many approaches have been proposed, but performance of them still has room for further improvement. Methodological issues: In our study, we propose a model stacking framework, which combines multiple base models. The protein features are extracted via PsePSSM from protein sequences. Features are fed into the base model including support vector machines (SVM), random forest (RF), XGBoost, etc. The results of base model are entered into logistic regression model for final process. Results: On the independent dataset, the accuracy and Matthew’s correlation coefficient (MCC) of proposed method are 95.70% and 0.8756, respectively. Furthermore, we show that the model stacking framework outperforms single machine learning classifiers statistically. Conclusion: Our models are better than most known strategies for identifying electron transport proteins. Our model can be used to more precisely identify electron transport proteins.

Keywords
Electron transport chain
Ensemble learning
Model stacking
Logistic regression
Transport protein
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