High functional coherence in k-partite protein cliques of protein interaction networks
Liu, Qian, Chen, Yi-Ping Phoebe and Li, Jinyan 2009, High functional coherence in k-partite protein cliques of protein interaction networks, in BIBM 2009 : Proccedings of the 2009 IEEE International Conference on Bioinformatics and Biomedicine, IEEE Computer Society, Piscataway, N. J., pp. 111-117, doi: 10.1109/BIBM.2009.46.
We introduce a new topological concept called k-partite protein cliques to study protein interaction (PPI) networks. In particular, we examine functional coherence of proteins in k-partite protein cliques. A k-partite protein clique is a k-partite maximal clique comprising two or more nonoverlapping protein subsets between any two of which full interactions are exhibited. In the detection of PPI’s k-partite maximal cliques, we propose to transform PPI networks into induced K-partite graphs with proteins as vertices where edges only exist among the graph’s partites. Then, we present a k-partite maximal clique mining (MaCMik) algorithm to enumerate k-partite maximal cliques from K-partite graphs. Our MaCMik algorithm is applied to a yeast PPI network. We observe that there does exist interesting and unusually high functional coherence in k-partite protein cliques—most proteins in k-partite protein cliques, especially those in the same partites, share the same functions. Therefore, the idea of k-partite protein cliques suggests a novel approach to characterizing PPI networks, and may help function prediction for unknown proteins.
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