Comparative Analysis of XXXX Gene Regulatory Networks via Hub Type Variation.

@entity5 @entity1 @entity263 @entity74
BACKGROUND: @entity263 is one of the most @entity5 in @entity1 and is fundamentally a @entity74 . Identifying regulators in @entity5 using novel systems biology approaches will potentially lead to new insight into this disease. It was sought to address this by inferring gene regulatory networks (GRNs). Moreover, dynamical analysis of GRNs can explain how regulators change among different conditions, such as @entity5 subtypes. METHODS: In our approach, independent gene regulatory networks from each prostate state were reconstructed using one of the current state-of-art reverse engineering approaches. Next, crucial genes involved in this @entity5 were highlighted by analyzing each network individually and also in comparison with each other. RESULTS: In this paper, a novel network-based approach was introduced to find critical transcription factors involved in @entity263 . The results led to detection of 38 essential transcription factors based on hub type variation. Additionally, experimental evidence was found for 29 of them as well as 9 new transcription factors. CONCLUSION: The results showed that dynamical analysis of biological networks may provide useful information to gain better understanding of the cell.
