ET-MSF: a model stacking framework to identify electron transport proteins.

Yizheng Wang, Qingfeng Pan, Xiaobin Liu, Yijie Ding

Frontiers in bioscience (Landmark edition) 2022 Jan 11

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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. 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. 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. © 2022 The Author(s). Published by IMR Press.

Citation

Yizheng Wang, Qingfeng Pan, Xiaobin Liu, Yijie Ding. ET-MSF: a model stacking framework to identify electron transport proteins. Frontiers in bioscience (Landmark edition). 2022 Jan 11;27(1):12