Abstract
BackgroundMalaria 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.MethodsIn 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.ResultsWe 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.
Highlights
Malaria is the most deadly parasitic infectious disease
To validate our method in prioritizing malaria genes, we performed a “leave-one-out” cross validation analysis and examined the ranks of a set of malaria genes extracted from literature
We investigated if the topranked genes represent opportunities for drug discovery for malaria
Summary
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 contributes in revealing the disease pathogenesis, and facilitates discovering new targets for anti-malaria drugs. Detecting the novel genetic basis for malaria reveals the disease pathogenesis, and facilitates discovering new targets for anti-malaria drugs [7,8,9,10,11]. After being injected by mosquitos into human skin, these parasites infect the liver and multiply using the host’s cell resources. They invade the red blood cells and cause
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