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首页> 外文期刊>BMC Genomics >Network-based gene prediction for Plasmodium falciparum malaria towards genetics-based drug discovery
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Network-based gene prediction for Plasmodium falciparum malaria towards genetics-based drug discovery

机译：基于网络的恶性疟原虫疟疾基因预测，以基于遗传学的药物发现

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Background Malaria is the most deadly parasitic infectious disease. Existing drug treatments have limited efficacy in malaria elimination, and the complex pathogenesis of the disease is not fully understood. Detecting novel malaria-associated genes not only contributes in revealing the disease pathogenesis, but also facilitates discovering new targets for anti-malaria drugs. Methods In this study, we developed a network-based approach to predict malaria-associated genes. We constructed a cross-species network to integrate human-human, parasite-parasite and human-parasite protein interactions. Then we extended the random walk algorithm on this network, and used known malaria genes as the seeds to find novel candidate genes for malaria. Results We validated our algorithms using 77 known malaria genes: 14 human genes and 63 parasite genes were ranked averagely within top 2% and top 4%, respectively among human and parasite genomes. We also evaluated our method for predicting novel malaria genes using a set of 27 genes with literature supporting evidence. Our approach ranked 12 genes within top 1% and 24 genes within top 5%. In addition, we demonstrated that top-ranked candied genes were enriched for drug targets, and identified commonalities underlying top-ranked malaria genes through pathway analysis. In summary, the candidate malaria-associated genes predicted by our data-driven approach have the potential to guide genetics-based anti-malaria drug discovery.
机译：背景疟疾是最致命的寄生虫传染病。现有的药物治疗在消除疟疾方面的功效有限，并且对该疾病的复杂发病机理还没有完全了解。检测新的疟疾相关基因不仅有助于揭示疾病的发病机理，而且还有助于发现抗疟​​疾药物的新靶标。方法在本研究中，我们开发了一种基于网络的方法来预测与疟疾相关的基因。我们构建了一个跨物种网络，以整合人与人，寄生虫-寄生虫和人-寄生虫蛋白质相互作用。然后，我们在该网络上扩展了随机游走算法，并使用已知的疟疾基因作为种子来寻找疟疾的新候选基因。结果我们使用77个已知的疟疾基因验证了我们的算法：14个人类基因和63个寄生虫基因在人类和寄生虫基因组中的平均排名分别在前2％和前4％之内。我们还评估了我们使用一组27种基因预测新的疟疾基因的方法，并有文献支持证据。我们的方法对排名前1％的12个基因和排名前5％的24个基因进行了排名。此外，我们证明了排名靠前的蜜饯基因可以丰富药物靶标，并通过途径分析确定了排名靠前的疟疾基因的共性。总之，通过我们的数据驱动方法预测的与疟疾有关的候选基因具有指导基于遗传学的抗疟疾药物发现的潜力。

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