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Int. J. Mol. Sci. 2017, 18(9), 1910; doi:10.3390/ijms18091910

Protein Complexes Prediction Method Based on Core—Attachment Structure and Functional Annotations

†,* and
College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Received: 25 August 2017 / Revised: 31 August 2017 / Accepted: 1 September 2017 / Published: 6 September 2017
(This article belongs to the Special Issue Special Protein Molecules Computational Identification)
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Abstract

Recent advances in high-throughput laboratory techniques captured large-scale protein–protein interaction (PPI) data, making it possible to create a detailed map of protein interaction networks, and thus enable us to detect protein complexes from these PPI networks. However, most of the current state-of-the-art studies still have some problems, for instance, incapability of identifying overlapping clusters, without considering the inherent organization within protein complexes, and overlooking the biological meaning of complexes. Therefore, we present a novel overlapping protein complexes prediction method based on core–attachment structure and function annotations (CFOCM), which performs in two stages: first, it detects protein complex cores with the maximum value of our defined cluster closeness function, in which the proteins are also closely related to at least one common function. Then it appends attach proteins into these detected cores to form the returned complexes. For performance evaluation, CFOCM and six classical methods have been used to identify protein complexes on three different yeast PPI networks, and three sets of real complexes including the Munich Information Center for Protein Sequences (MIPS), the Saccharomyces Genome Database (SGD) and the Catalogues of Yeast protein Complexes (CYC2008) are selected as benchmark sets, and the results show that CFOCM is indeed effective and robust for achieving the highest F-measure values in all tests. View Full-Text
Keywords: protein–protein interaction network; overlapping; clustering protein–protein interaction network; overlapping; clustering
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Li, B.; Liao, B. Protein Complexes Prediction Method Based on Core—Attachment Structure and Functional Annotations. Int. J. Mol. Sci. 2017, 18, 1910.

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