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The identification of essential proteins in protein-protein interactionnetworks (PINs) can help to discover drug targets and prevent disease. In orderto improve the accuracy of the identification of essential proteins,researchers attempted to obtain a refined PIN by combining multiple biologicalinformation to filter out some unreliable interactions in the PIN.Unfortunately, such approaches drastically reduce the number of nodes in thePIN after multiple refinements and result in a sparser PIN. It makes aconsiderable portion of essential proteins unidentifiable. In this paper, wepropose a multi-layer refined network (MR-PIN) that addresses this problem.Firstly, four refined networks are constructed by respectively integratingdifferent biological information into the static PIN to form a multi-layerheterogeneous network. Then scores of proteins in each network layer arecalculated by the existing node ranking method, and the importance score of aprotein in the MR-PIN is evaluated in terms of the geometric mean of its scoresin all layers. Finally, all nodes are sorted by their importance scores todetermine their essentiality. To evaluate the effectiveness of the multi-layerrefined network model, we apply 16 node ranking methods on the MR-PIN, andcompare the results with those on the SPIN, DPIN and RDPIN. Then the predictiveperformances of these ranking methods are validated in terms of theidentification number of essential protein at top100 - top600, sensitivity,specificity, positive predictive value, negative predictive value, F-measure,accuracy, Jackknife, ROCAUC and PRAUC. The experimental results show that theMR-PIN is superior to the existing refined PINs in the identification accuracyof essential proteins.

A multi-layer refined network model for the identification of essential proteins