Gene co-expression networks can be used to associate genes of unknown function with biological processes or to find genes in a specific context, environment responsible for a disease. We provide an overview of methods and tools used to identify such recurrent patterns across multiple networks, can be used to discover biological modules in co-expression networks constructed from gene expression data and we explain how this can be used to identify genes with a regulatory role in disease. However, existing algorithms are very much costly in terms of time and space. As network size or number increases, mining such modules get much more complex. We have developed an efficient approach to mine such recurrent context specific modules from 35 gene networks. This computationally very difficult problem due to the exponential number of patterns was solved non-exponentially.