VTP-Identifier: Vesicular Transport Proteins Identification Based on PSSM Profiles and XGBoost

Yue Gong; Benzhi Dong; Zixiao Zhang; Yixiao Zhai; Bo Gao; Tianjiao Zhang; Jingyu Zhang

doi:10.3389/fgene.2021.808856

VTP-Identifier: Vesicular Transport Proteins Identification Based on PSSM Profiles and XGBoost

Front Genet. 2022 Jan 3:12:808856. doi: 10.3389/fgene.2021.808856. eCollection 2021.

Authors

Yue Gong¹, Benzhi Dong¹, Zixiao Zhang¹, Yixiao Zhai¹, Bo Gao², Tianjiao Zhang¹, Jingyu Zhang³

Affiliations

¹ College of Information and Computer Engineering, Northeast Forestry University, Harbin, China.
² Department of Radiology, The Second Affiliated Hospital, Harbin Medical University, Harbin, China.
³ Department of Neurology, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, China.

Abstract

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.

Keywords: XGBoost; machine learning; position-specific scoring matrix; protein function prediction; vesicular transport proteins.

Grants and funding

This work was supported by the Fundamental Research Funds for the Central Universities (2572021BH01) and the National Natural Science Foundation of China (62172087, 62172129).