Protein complexes detection based on node local properties and gene expression in PPI weighted networks

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Abstract

Background: Identifying protein complexes from protein–protein interaction (PPI) networks is a crucial task, and many related algorithms have been developed. Most algorithms usually employ direct neighbors of nodes and ignore resource allocation and second-order neighbors. The effective use of such information is crucial to protein complex detection. Result: Based on this observation, we propose a new way by combining node resource allocation and gene expression information to weight protein network (NRAGE-WPN), in which protein complexes are detected based on core-attachment and second-order neighbors. Conclusions: Through comparison with eleven methods in Yeast and Human PPI network, the experimental results demonstrate that this algorithm not only performs better than other methods on 75% in terms of f-measure+, but also can achieve an ideal overall performance in terms of a composite score consisting of five performance measures. This identification method is simple and can accurately identify more complexes.

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Yu, Y., & Kong, D. (2022). Protein complexes detection based on node local properties and gene expression in PPI weighted networks. BMC Bioinformatics, 23(1). https://doi.org/10.1186/s12859-021-04543-4

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