Reconstructing gene networks from microarray data has provided mechanistic information on cellular processes. A popular structure learning method, Bayesian network inference, has been used to determine network topology despite its shortcomings, i.e. the high-computational cost when analyzing a large number of genes and the inefficiency in exploiting prior knowledge, such as the co-regulation information of the genes. To address these limitations, we are introducing an alternative method, knowledge-driven matrix factorization (KMF) framework, to reconstruct phenotype-specific modular gene networks.