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

• Published in: Frontiers in genetics Vol. 12; p. 808856
• Main Authors: Gong, Yue, Dong, Benzhi, Zhang, Zixiao, Zhai, Yixiao, Gao, Bo, Zhang, Tianjiao, Zhang, Jingyu
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 03-01-2022
• Subjects: Genetics; machine learning; position-specific scoring matrix; protein function prediction; vesicular transport proteins; XGBoost; vesicular transport proteins; position-specific scoring matrix; machine learning; protein function prediction; XGBoost
• ISSN: 1664-8021; 1664-8021
• DOI: 10.3389/fgene.2021.808856

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.