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VTP-Identifier: Vesicular Transport Proteins Identification Based on PSSM Profiles and XGBoost
- Source :
- Frontiers in Genetics, Vol 12 (2022)
- Publication Year :
- 2022
- Publisher :
- Frontiers Media S.A., 2022.
-
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.
Details
- Language :
- English
- ISSN :
- 16648021
- Volume :
- 12
- Database :
- Directory of Open Access Journals
- Journal :
- Frontiers in Genetics
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.fd45c52a75514dfc900efb57bdaa9f03
- Document Type :
- article
- Full Text :
- https://doi.org/10.3389/fgene.2021.808856