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Phenome-driven disease genetics prediction toward drug discovery.

Chen Y, Li L, Zhang GQ, Xu R - Bioinformatics (2015)

Bottom Line: Our approach achieved significantly improved performance over a baseline method, which used only one phenotype data source.We also found literature evidence to support a number of drugs among the top 200 candidates.In summary, we demonstrated that a novel strategy combining unique disease phenotype data with system approaches can lead to rapid drug discovery. nlp. edu/public/data/DMN

View Article: PubMed Central - PubMed

Affiliation: Department of Electrical Engineering and Computer Science, Department of Epidemiology and Biostatistics and Department of Family Medicine and Community Health, Case Western Reserve University, Cleveland, OH 44106, USA.

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Network integration
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btv245-F1: Network integration

Mentions: We integrated DMN, mimMiner and a genetic network based on protein–protein interactions (PPIs), and constructed a heterogeneous network in Figure 1. Given a disease, we prioritized the genes using a ranking algorithm extended from the random walk model. We validated our approach using well-studied disease–gene associations from OMIM and compared the performance with a baseline disease gene prediction method that used only one phenotype network. We also evaluated our approach in predicting genes for diseases of different classes. Finally, we identified candidate drug therapies for Crohn’s disease based on gene prediction results, and demonstrated the translational potential of our newly predicted genes.Fig. 1.


Phenome-driven disease genetics prediction toward drug discovery.

Chen Y, Li L, Zhang GQ, Xu R - Bioinformatics (2015)

Network integration
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4542779&req=5

btv245-F1: Network integration
Mentions: We integrated DMN, mimMiner and a genetic network based on protein–protein interactions (PPIs), and constructed a heterogeneous network in Figure 1. Given a disease, we prioritized the genes using a ranking algorithm extended from the random walk model. We validated our approach using well-studied disease–gene associations from OMIM and compared the performance with a baseline disease gene prediction method that used only one phenotype network. We also evaluated our approach in predicting genes for diseases of different classes. Finally, we identified candidate drug therapies for Crohn’s disease based on gene prediction results, and demonstrated the translational potential of our newly predicted genes.Fig. 1.

Bottom Line: Our approach achieved significantly improved performance over a baseline method, which used only one phenotype data source.We also found literature evidence to support a number of drugs among the top 200 candidates.In summary, we demonstrated that a novel strategy combining unique disease phenotype data with system approaches can lead to rapid drug discovery. nlp. edu/public/data/DMN

View Article: PubMed Central - PubMed

Affiliation: Department of Electrical Engineering and Computer Science, Department of Epidemiology and Biostatistics and Department of Family Medicine and Community Health, Case Western Reserve University, Cleveland, OH 44106, USA.

Show MeSH
Related in: MedlinePlus