Stack-VTP: prediction of vesicle transport proteins based on stacked ensemble classifier and evolutionary information

Abstract

Vesicle transport proteins not only play an important role in the transmembrane transport of molecules, but also have a place in the field of biomedicine, so the identification of vesicle transport proteins is particularly important. We propose a method based on ensemble learning and evolutionary information to identify vesicle transport proteins. Firstly, we preprocess the imbalanced dataset by random undersampling. Secondly, we extract position-specific scoring matrix (PSSM) from protein sequences, and then further extract AADP-PSSM and RPSSM features from PSSM, and use the Max-Relevance-Max-Distance (MRMD) algorithm to select the optimal feature subset. Finally, the optimal feature subset is fed into the stacked classifier for vesicle transport proteins identification. The experimental results show that the of accuracy (ACC), sensitivity (SN) and specificity (SP) of our method on the independent testing set are 82.53%, 0.774 and 0.836, respectively. The SN, SP and ACC of our proposed method are 0.013, 0.007 and 0.76% higher than the current state-of-the-art methods.

Keywords: Ensemble learning; Protein prediction; Stacked model; Vesicle transport proteins.

Fig. 1

The flowchart of the vesicle transport proteins prediction model

Fig. 2

The Stacked ensemble classifier framework

Fig. 3

Comparison of different feature extraction methods

Fig. 4

ROC curves for 10-fold cross-validation of independent classifiers

Fig. 5

ROC curves of independent classifiers on testing set

Fig. 6

ROC curves for 10-fold cross-validation of different meta-classifiers on the training set

Fig. 7

ROC curves of different meta-classifiers on testing set

Fig. 8

Performance of different dimensionality reduction methods on the training and testing set