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The identification of essential proteins can help in understanding theminimum requirements for cell survival and development. Network-basedcentrality approaches are commonly used to identify essential proteins fromprotein-protein interaction networks (PINs). Unfortunately, these approachesare limited by the poor quality of the underlying PIN data. To overcome thisproblem, researchers have focused on the prediction of essential proteins bycombining PINs with other biological data. In this paper, we proposed a networkrefinement method based on module discovery and biological information toobtain a higher quality PIN. First, to extract the maximal connected subgraphin the PIN and to divide it into different modules by using Fast-unfoldingalgorithm; then, to detect critical modules based on the homology information,subcellular localization information and topology information within eachmodule, and to construct a more refined network (CM-PIN). To evaluate theeffectiveness of the proposed method, we used 10 typical network-basedcentrality methods (LAC, DC, DMNC, NC, TP, LID, CC, BC, PR, LR) to compare theoverall performance of the CM-PIN with those the refined dynamic proteinnetwork (RD-PIN). The experimental results showed that the CM-PIN was optimalin terms of precision-recall curve, jackknife curve and other criteria, and canhelp to identify essential proteins more accurately.

A protein network refinement method based on module discovery and biological information