Bian Haodong, Guo Maozu, Wang Juan
School of Computer Science, Inner Mongolia University, Hohhot, China.
School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing, China.
Front Cell Dev Biol. 2020 Sep 16;8:578901. doi: 10.3389/fcell.2020.578901. eCollection 2020.
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 ( 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 . 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.
线粒体在真核细胞中发挥着重要作用，特别是在疟原虫细胞中。它们具有一些不同寻常的进化和功能特征，这些特征对于疾病诊断和药物设计极为关键。因此，预测疟原虫的线粒体蛋白已成为一项有价值的工作。然而，现有的计算方法只能预测恶性疟原虫（简称P. falciparum）的线粒体蛋白，并且这些方法的准确性较低。非常需要设计一种高精度的分类器，用于预测所有疟原虫物种的线粒体蛋白，而不仅仅是恶性疟原虫。我们提出了一种名为PM-OTC的新方法，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，并选择三肽组成作为特征。我们采用5折交叉验证方法来训练和测试PM-OTC。结果表明，PM-OTC的准确率达到了94.91%，并且其性能优于其他方法。
