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Vesicular transport proteins are related to many human diseases, and they threaten human health when they undergo pathological changes. Protein function prediction has been one of the most in-depth topics in bioinformatics. In this work, we developed a useful tool to identify vesicular transport proteins. Our strategy is to extract transition probability composition, autocovariance transformation and other information from the position-specific scoring matrix as feature vectors. EditedNearesNeighbours (ENN) is used to address the imbalance of the data set, and the Max-Relevance-Max-Distance (MRMD) algorithm is adopted to reduce the dimension of the feature vector. We used 5-fold cross-validation and independent test sets to evaluate our model. On the test set, VTP-Identifier presented a higher performance compared with GRU. The accuracy, Matthew's correlation coefficient (MCC) and area under the ROC curve (AUC) were 83.6%, 0.531 and 0.873, respectively. Copyright © 2022 Gong, Dong, Zhang, Zhai, Gao, Zhang and Zhang.


Yue Gong, Benzhi Dong, Zixiao Zhang, Yixiao Zhai, Bo Gao, Tianjiao Zhang, Jingyu Zhang. VTP-Identifier: Vesicular Transport Proteins Identification Based on PSSM Profiles and XGBoost. Frontiers in genetics. 2021;12:808856

PMID: 35047020

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