From: Predicting disease associations via biological network analysis
Data | Size | Evaluation | |
---|---|---|---|
van Driel et al. (2006) [4] | OMIM | 5132 phenotypes in OMIM | Comparing results with genotypic similarities |
Lage et al. (2007) [5] | OMIM | 7000 OMIM record pairs | Evaluating results against the overlap of the OMIM record pairs |
Goh et al. (2007) [6] | OMIM | 1284 OMIM diseases | Analysing network topologicalproperties |
Huang et al. (2009) [12] | GWAS | 7 diseases | Comparing results with phenotypic similarities |
Li and Agarwal (2009) [7] | Pubmed abstracts,biological pathways | 1028 diseases in MeSH | Comparing results with MeSHclassification |
Kim et al. (2009) [13] | GWAS | 53 clinical traits related tosevere asthma | Mining the literature manually |
Hu and Agarwal (2009) [8] | Expression data | 645 diseases in MeSH | Comparing results with MeSHclassification |
Suthram et al. (2010) [9] | Expression data, PPI | 54 diseases | Evaluating results against genetic similarities |
Lewis et al. (2011) [14] | GWAS | 61 diseases | Comparing results with Huang et al.(2009) results |
Mathur and Dinakarpandian et al.(2007) [10] | DO annotation, GOannotation | 36 diseases (for evaluation) | Evaluating results using 68 curated disease associations |
Our study | Disease-gene associations, GOannotation, PPI | 543 ICD-9 diseases | Evaluating results against ICD-9classification, comorbidity, andgenetic similarities derived fromGWAS data |