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• W2895946680 abstract "Computational methods are often applied to identify essential proteins from protein-protein interaction networks. In this paper, inspected by node and edge clustering coefficient (NEC) and PeC, we propose an improved version of node and edge clustering coefficient (INEC) which both considers dual topological characteristics of the network and high false positives of the protein-protein interaction data. We apply it for the identification of essential proteins. And we implement three versions of INEC which combine different biological information. In order not to be confused, we call the first one INEC0 which dosen't integrate biological information, the second one INEC1 which integrates gene expression similarity, and the third one INEC2 which integrates gene expression similarity, functional similarity, and protein-protein sequence similarity. We apply three implemented INEC methods to protein-protein interaction data of Saccharomyces cerevisiae (Yeast) and compare them with some state-of-the-art methods (DC, NC, PeC, and NEC). The experimental results show that our proposed methods achieve better results in terms of prediction accuracy, area under the curve of PR-curve, and Jackknife methodology." @default.
• W2895946680 created "2018-10-26" @default.
• W2895946680 creator A5074231648 @default.
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• W2895946680 date "2018-07-01" @default.
• W2895946680 modified "2023-09-25" @default.
• W2895946680 title "Identification of Essential Proteins Using Improved Node and Edge Clustering Coefficient" @default.
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• W2895946680 doi "https://doi.org/10.23919/chicc.2018.8483322" @default.
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