Limits...
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.

Show MeSH

Related in: MedlinePlus

(A1, A2) Evaluate our gene rank with the genes associated with Crohn’s disease from GWAS. (B1, B2) Evaluate our gene rank with the drug target genes. (C1, C2) Evaluate our drug rank with the FDA-approved drugs
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4542779&req=5

btv245-F6: (A1, A2) Evaluate our gene rank with the genes associated with Crohn’s disease from GWAS. (B1, B2) Evaluate our gene rank with the drug target genes. (C1, C2) Evaluate our drug rank with the FDA-approved drugs

Mentions: We ranked the 9465 genes in the PPI network for Crohn’s disease and compared the result with 70 genes associated with Crohn’s disease from GWAS catalog. These 70 genes also appeared in our gene rank, and have no overlap with the data in OMIM. Figure 6A1 shows that the number of GWAS genes drops when the rank based on our approach changes from the top to the bottom, while this number distributes evenly among random ranks (Fig. 6A2). Among the top 10% in our rank, we found 19 overlaps with the GWAS genes, which is a 2.5-fold enrichment (P < e−4) compared with the average of 50 random gene ranks. The result shows that our approach can prioritize the disease-associated genes obtained through statistical analysis on large-scale patient data.Fig. 6.


Phenome-driven disease genetics prediction toward drug discovery.

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

(A1, A2) Evaluate our gene rank with the genes associated with Crohn’s disease from GWAS. (B1, B2) Evaluate our gene rank with the drug target genes. (C1, C2) Evaluate our drug rank with the FDA-approved drugs
© Copyright Policy - creative-commons
Related In: Results  -  Collection

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

btv245-F6: (A1, A2) Evaluate our gene rank with the genes associated with Crohn’s disease from GWAS. (B1, B2) Evaluate our gene rank with the drug target genes. (C1, C2) Evaluate our drug rank with the FDA-approved drugs
Mentions: We ranked the 9465 genes in the PPI network for Crohn’s disease and compared the result with 70 genes associated with Crohn’s disease from GWAS catalog. These 70 genes also appeared in our gene rank, and have no overlap with the data in OMIM. Figure 6A1 shows that the number of GWAS genes drops when the rank based on our approach changes from the top to the bottom, while this number distributes evenly among random ranks (Fig. 6A2). Among the top 10% in our rank, we found 19 overlaps with the GWAS genes, which is a 2.5-fold enrichment (P < e−4) compared with the average of 50 random gene ranks. The result shows that our approach can prioritize the disease-associated genes obtained through statistical analysis on large-scale patient data.Fig. 6.

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