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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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The ROC curves and AUCs for our method (red) and the baseline method (blue) in the leave-one-out cross-validation analysis
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btv245-F2: The ROC curves and AUCs for our method (red) and the baseline method (blue) in the leave-one-out cross-validation analysis

Mentions: Our approach achieved significantly better success ratios and AUCs than the baseline method. The integrated network in our approach contains a total of 2397 unique disease–gene associations. If one disease appeared in the two phenotype networks and were connected to a same gene, the two disease-gene links were counted only once. In 1100 of the 2397 validation runs (45.89%), our approach successfully ranked the retained genes in top 1. The success ratio is significantly higher (P < e−4) than 10.36% for the baseline method (Table 1). In addition, Figure 2 compares the ROC curves for gene prediction methods. Our approach achieved an AUC of 90.65%, which is significantly higher (P < e−4) than 84.2% for the baseline approach.Fig. 2.


Phenome-driven disease genetics prediction toward drug discovery.

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

The ROC curves and AUCs for our method (red) and the baseline method (blue) in the leave-one-out cross-validation analysis
© Copyright Policy - creative-commons
Related In: Results  -  Collection

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

btv245-F2: The ROC curves and AUCs for our method (red) and the baseline method (blue) in the leave-one-out cross-validation analysis
Mentions: Our approach achieved significantly better success ratios and AUCs than the baseline method. The integrated network in our approach contains a total of 2397 unique disease–gene associations. If one disease appeared in the two phenotype networks and were connected to a same gene, the two disease-gene links were counted only once. In 1100 of the 2397 validation runs (45.89%), our approach successfully ranked the retained genes in top 1. The success ratio is significantly higher (P < e−4) than 10.36% for the baseline method (Table 1). In addition, Figure 2 compares the ROC curves for gene prediction methods. Our approach achieved an AUC of 90.65%, which is significantly higher (P < e−4) than 84.2% for the baseline approach.Fig. 2.

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