Network-based gene prediction for Plasmodium falciparum malaria towards genetics-based drug discovery

BMC Genomics

Research

Yang Chen¹  Rong Xu² 

[1] Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH, USA;Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH, USA;

关键词: malaria;    disease gene prediction;    network analysis;    drug discovery;   

DOI  :  10.1186/1471-2164-16-S7-S9

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY   
© Chen and Xu; licensee BioMed Central Ltd. 2015