Abstract
As a parasitic protozoan, Plasmodium falciparum (P. falciparum) can cause malaria. The mitochondrial proteins of malaria parasite play important roles in the discovery of anti-malarial drug targets. Thus, accurate identification of mitochondrial proteins of malaria parasite is a key step for understanding their functions and finding potential drug targets. In this work, we developed a sequence-based method to identify the mitochondrial proteins of malaria parasite. At first, we extended adjoining dipeptide composition to g-gap dipeptide composition for discretely formulating the protein sequences. Subsequently, the analysis of variance (ANOVA) combined with incremental feature selection (IFS) was used to pick out the optimal features. Finally, the jackknife cross-validation was used to evaluate the performance of the proposed model. Evaluation results showed that the maximum accuracy of 97.1 % could be achieved by using 101 optimal 5-gap dipeptides. The comparison with previous methods demonstrated that our method was accurate and efficient.
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Abbreviations
- ANOVA:
-
Analysis of variance
- auROC:
-
Area under the receiver operating characteristic
- IFS:
-
Incremental feature selection
- MCC:
-
Matthews correlation coefficient
- ROC:
-
Receiver operating characteristic
- Sn:
-
Sensitivity
- Sp:
-
Specificity
- Acc:
-
Overall accuracy
- SVM:
-
Support vector machine
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Acknowledgments
We would like to thank the anonymous reviewers for their valuable suggestions. This work was supported by the National Nature Scientific Foundation of China (No. 61301260) and the Fundamental Research Funds for the Central Universities (No. ZYGX2012J113).
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The authors declare that there is no conflict of interest.
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Ding, H., Li, D. Identification of mitochondrial proteins of malaria parasite using analysis of variance. Amino Acids 47, 329–333 (2015). https://doi.org/10.1007/s00726-014-1862-4
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DOI: https://doi.org/10.1007/s00726-014-1862-4