Abstract
Identifying essential proteins is important for understanding the minimal requirements for cellular survival and development. Fast growth in the amount of available protein-protein interactions has produced unprecedented opportunities for detecting protein essentiality on network level. A series of centrality measures have been proposed to discover essential proteins based on network topology. However, most of them treat all interactions equally and are sensitive to false positives. In this paper, six standard centrality measures are redefined to be used in weighted network. A new method for weighing protein-protein interactions is proposed based on the combination of logistic regression-based model and function similarity. The experimental results on yeast network show that the weighting method can improve the performance of centrality measures considerably. More essential proteins are discovered by the weighted centrality measures than by the original centrality measures used in unweighted network. Even about 20% improvements are obtained from closeness centrality and subgraph centrality.
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