  The identification of essential proteins can help in understanding the
minimum requirements for cell survival and development. Network-based
centrality approaches are commonly used to identify essential proteins from
protein-protein interaction networks (PINs). Unfortunately, these approaches
are limited by the poor quality of the underlying PIN data. To overcome this
problem, researchers have focused on the prediction of essential proteins by
combining PINs with other biological data. In this paper, we proposed a network
refinement method based on module discovery and biological information to
obtain a higher quality PIN. First, to extract the maximal connected subgraph
in the PIN and to divide it into different modules by using Fast-unfolding
algorithm; then, to detect critical modules based on the homology information,
subcellular localization information and topology information within each
module, and to construct a more refined network (CM-PIN). To evaluate the
effectiveness of the proposed method, we used 10 typical network-based
centrality methods (LAC, DC, DMNC, NC, TP, LID, CC, BC, PR, LR) to compare the
overall performance of the CM-PIN with those the refined dynamic protein
network (RD-PIN). The experimental results showed that the CM-PIN was optimal
in terms of precision-recall curve, jackknife curve and other criteria, and can
help to identify essential proteins more accurately.
