School of Basic Medical Sciences, Southwest Medical University, Luzhou, China.
Department of Computer Science, Pacific Lutheran University, Tacoma, Washington, USA.
Curr Med Chem. 2022;29(5):807-821. doi: 10.2174/0929867328666211005140625.
Malaria caused by Plasmodium falciparum is one of the major infectious diseases in the world. It is essential to exploit an effective method to predict secretory proteins of malaria parasites to develop effective cures and treatment. Biochemical assays can provide details for accurate identification of the secretory proteins, but these methods are expensive and time-consuming. In this paper, we summarized the machine learningbased identification algorithms and compared the construction strategies between different computational methods. Also, we discussed the use of machine learning to improve the ability of algorithms to identify proteins secreted by malaria parasites.
由疟原虫引起的疟疾是世界上主要的传染病之一。开发一种有效的方法来预测疟原虫的分泌蛋白对于开发有效的治疗方法至关重要。生化分析可以为准确识别分泌蛋白提供详细信息，但这些方法既昂贵又耗时。在本文中，我们总结了基于机器学习的鉴定算法，并比较了不同计算方法之间的构建策略。此外，我们还讨论了使用机器学习来提高算法识别疟原虫分泌蛋白的能力。
