Mitochondria play essential roles in eukaryotic cells, especially in Plasmodium cells. They have several unusual evolutionary and functional features that are incredibly vital for disease diagnosis and drug design. Thus, predicting mitochondrial proteins of Plasmodium has become a worthwhile work. However, existing computational methods can only predict mitochondrial proteins of Plasmodium falciparum (P. falciparum for short), and these methods have low accuracy. It is highly desirable to design a classifier with high accuracy for predicting mitochondrial proteins for all Plasmodium species, not only P. falciparum. We proposed a novel method, named as PM-OTC, for predicting mitochondrial proteins in Plasmodium. PM-OTC uses the Support Vector Machine (SVM) as the classifier and the selected tripeptide composition as the features. We adopted the 5-fold cross-validation method to train and test PM-OTC. Results demonstrate that PM-OTC achieves an accuracy of 94.91%, and performances of PM-OTC are superior to other methods.

线粒体在真核细胞，尤其是在疟原虫细胞中起重要作用。它们具有一些异常的进化和功能特征，这些特征对于疾病诊断和药物设计至关重要。因此，预测疟原虫的线粒体蛋白已成为一项有价值的工作。但是，现有的计算方法只能预测线粒体蛋白的恶性疟原虫 （恶性疟原虫简称），而且这些方法的准确性较低。设计高度准确的分类器以预测所有疟原虫物种的线粒体蛋白质是非常理想的恶性疟原虫。我们提出了一种新的方法，称为PM-OTC，用于预测疟原虫中的线粒体蛋白。PM-OTC使用支持向量机（SVM）作为分类器，并使用选定的三肽组成作为特征。我们采用5倍交叉验证方法来训练和测试PM-OTC。结果表明，PM-OTC的准确度达到94.91％，并且PM-OTC的性能优于其他方法。