Here we report on a meta-analysis approach that combines high-throughput data of heterogeneous origin in the domain of type 2 diabetes mellitus (T2DM), in particular in connection with obesity as a risk factor. Different data sources such as DNA microarrays, ChIP on chip and qualitative data from multiple tissues from human and mouse are integrated and validated by a scoring system in order to assign disease relevance to the genes. Using a random sampling approach we computed a set of 213 genes most relevant for obesity-induced T2DM. Furthermore, we extrapolated functional information on cellular networks associated with these genes such as pathway information, protein-protein interactions and gene regulatory networks. In order to allow users to derive T2DM relevance for any given gene we have set up a web interface that allows the screening of the gene in the light of the underlying data (T2DM-GeneMiner http://t2dm-geneminer.molgen.mpg.de).