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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 for each disease class in leave-one-out cross-validation. We compared the top part of ROC curves and AUC scores based on the top 100 genes in each validation run
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btv245-F5: The ROC curves for each disease class in leave-one-out cross-validation. We compared the top part of ROC curves and AUC scores based on the top 100 genes in each validation run

Mentions: In the de novo gene prediction, we observed similar performance variance among the nine disease classes. Figure 5 shows that the averaged AUC is the highest for congenital malformations and deformations and lowest for malignant neoplasms at all cutoffs. Table 2 (the column of ‘Monogenetic diseases’) shows that for monogenetic diseases, which have only one gene in OMIM, 90% predictions ranked the disease genes for congenital malformations and deformations in top 1, while 50% predictions succeeded for malignant neoplasms.Fig. 5.


Phenome-driven disease genetics prediction toward drug discovery.

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

The ROC curves for each disease class in leave-one-out cross-validation. We compared the top part of ROC curves and AUC scores based on the top 100 genes in each validation run
© Copyright Policy - creative-commons
Related In: Results  -  Collection

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

btv245-F5: The ROC curves for each disease class in leave-one-out cross-validation. We compared the top part of ROC curves and AUC scores based on the top 100 genes in each validation run
Mentions: In the de novo gene prediction, we observed similar performance variance among the nine disease classes. Figure 5 shows that the averaged AUC is the highest for congenital malformations and deformations and lowest for malignant neoplasms at all cutoffs. Table 2 (the column of ‘Monogenetic diseases’) shows that for monogenetic diseases, which have only one gene in OMIM, 90% predictions ranked the disease genes for congenital malformations and deformations in top 1, while 50% predictions succeeded for malignant neoplasms.Fig. 5.

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