Predicting disease-related subnetworks for type 1 diabetes using a new network activity score.

ABSTRACT: In this study we investigated the advantage of including network information in prioritizing disease genes of type 1 diabetes (T1D). First, a naïve Bayesian network (NBN) model was developed to integrate information from multiple data sources and to define a T1D-involvement probability score (PS) for each individual gene. The algorithm was validated using known functional candidate genes as a benchmark. Genes with higher PS were found to be more likely to appear in T1D-related publications. Next a new network activity metric was proposed to evaluate the T1D relevance of protein-protein interaction (PPI) subnetworks. The metric considered the contribution both from individual genes and from network topological characteristics. The predictions were confirmed by several independent datasets,

SUBMITTER: Gao S 

PROVIDER: S-EPMC3459426 | biostudies-literature | 2012 Oct

REPOSITORIES: biostudies-literature