ArticleBMC genomics2015
Network-based gene prediction for Plasmodium falciparum malaria towards genetics-based drug discovery.
Article in BMC genomics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Review
- Network-driven analysis of human-Plasmodium falciparum interactome: processes for malaria drug discovery and extracting in silico targets.Malaria journal · 2021Article
- A Drug-Side Effect Context-Sensitive Network approach for drug target prediction.Bioinformatics (Oxford, England) · 2019Article
- Context-sensitive network analysis identifies food metabolites associated with Alzheimer's disease: an exploratory study.BMC medical genomics · 2019Article
- Using a novel computational drug-repositioning approach (DrugPredict) to rapidly identify potent drug candidates for cancer treatment.Oncogene · 2018Article
- Context-sensitive network-based disease genetics prediction and its implications in drug discovery.Bioinformatics (Oxford, England) · 2017Article
- Drug repositioning for prostate cancer: using a data-driven approach to gain new insights.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2017Article
- Interrogating Patient-level Genomics and Mouse Phenomics towards Understanding Cytokines in Colorectal Cancer Metastasis.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2017Article
- Disease gene prioritization by integrating tissue-specific molecular networks using a robust multi-network model.BMC bioinformatics · 2016Article
- Phenome-based gene discovery provides information about Parkinson's disease drug targets.BMC genomics · 2016Article
- Towards understanding brain-gut-microbiome connections in Alzheimer's disease.BMC systems biology · 2016Article
- A genomics-based systems approach towards drug repositioning for rheumatoid arthritis.BMC genomics · 2016Article
- Towards precision medicine-based therapies for glioblastoma: interrogating human disease genomics and mouse phenotypes.BMC genomics · 2016Article
- A Multilayer Network Approach for Guiding Drug Repositioning in Neglected Diseases.PLoS neglected tropical diseases · 2016Article
- Explore Small Molecule-induced Genome-wide Transcriptional Profiles for Novel Inflammatory Bowel Disease Drug.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2016Article
- Combining Human Disease Genetics and Mouse Model Phenotypes towards Drug Repositioning for Parkinson's disease.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2015Article
- DenguePredict: An Integrated Drug Repositioning Approach towards Drug Discovery for Dengue.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2015Article
- Education, collaboration, and innovation: intelligent biology and medicine in the era of big data.BMC genomics · 2015Article
- Network-Based Analysis of Fatal Comorbidities of COVID-19 and Potential Therapeutics.IEEE/ACM transactions on computational biology and bioinformaticsArticle
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2 authors.
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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.
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