ArticleBMC genomics2016
Phenome-based gene discovery provides information about Parkinson's disease drug targets.
Article in BMC genomics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Combining phenome-driven drug-target interaction prediction with patients' electronic health records-based clinical corroboration toward drug discovery.Bioinformatics (Oxford, England) · 2020Article
- Predicting links between tumor samples and genes using 2-Layered graph based diffusion approach.BMC bioinformatics · 2019Article
- A Drug-Side Effect Context-Sensitive Network approach for drug target prediction.Bioinformatics (Oxford, England) · 2019Article
- The Alzheimer's comorbidity phenome: mining from a large patient database and phenome-driven genetics prediction.JAMIA open · 2019Article
- Using a novel computational drug-repositioning approach (DrugPredict) to rapidly identify potent drug candidates for cancer treatment.Oncogene · 2018Article
- Predict drug permeability to blood-brain-barrier from clinical phenotypes: drug side effects and drug indications.Bioinformatics (Oxford, England) · 2017Article
- Parkinson's Disease: From Pathogenesis to Pharmacogenomics.International journal of molecular sciences · 2017Review
- Combining mechanism-based prediction with patient-based profiling for psoriasis metabolomics biomarker discovery.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2017Article
- Disease gene prioritization by integrating tissue-specific molecular networks using a robust multi-network model.BMC bioinformatics · 2016Article
- Combining Human Disease Genetics and Mouse Model Phenotypes towards Drug Repositioning for Parkinson's disease.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2015Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
backgroundParkinson disease (PD) is a severe neurodegenerative disease without curative drugs. The highly complex and heterogeneous disease mechanisms are still unclear. Detecting novel PD associated genes not only contributes in revealing the disease pathogenesis, but also facilitates discovering new targets for drugs.
methodsWe propose a phenome-based gene prediction strategy to identify disease-associated genes for PD. We integrated multiple disease phenotype networks, a gene functional relationship network, and known PD genes to predict novel candidate genes. Then we investigated the translational potential of the predicted genes in drug discovery.
resultsIn a cross validation analysis, the average rank for 15 known PD genes is within top 0.8 %. We also tested the algorithm with an independent validation set of 669 PD-associated genes detected by genome-wide association studies. The top ranked genes predicted by our approach are enriched for these validation genes. In addition, our approach prioritized the target genes for FDA-approved PD drugs and the drugs that have been tested for PD in clinical trials. Pathway analysis shows that the prioritized drug target genes are closely associated with PD pathogenesis. The result provides empirical evidence that our computational gene prediction approach identifies novel candidate genes for PD, and has the potential to lead to rapid drug discovery.
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