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Characterization and identification of protein complexes in protein-protein interaction (PPI) networks is important in understanding cellular processes. With the core-attachment concept, a novel core-attachment algorithm is proposed by characterizing the protein complex core from the perspective of edges. We reinvite a protein complex core to be a set of closely interrelated edges rather than a set...
Identifying modules in protein-protein interaction (PPI) networks is important to understand the organization of the cellular processes. In this paper, an improved algorithm based on affinity propagation (AP) is proposed. We embed AP in our algorithm by utilizing AP to find the candidate overlapping vertices and keep those satisfying our filter condition. We apply our algorithm to S. cerevisiae PPI...
In this paper, we propose an average-degree based cluster mining algorithm (ACM) for complexes detection in PPI networks. ACM method contains of three stages. Firstly, we make use of PPI network topology, i.e., average degree, to present a new quantitative function and then present a hierarchical algorithm to identify protein complexes. Finally, post-processing is applied to the predicted results...
PPI(Protein-protein interaction) networks decomposition is of great importance for understanding and detecting functional complexes in PPI networks. In this paper, we study spectral clustering for detecting protein complexes, focusing on two open issues in spectral clustering: (i) constructing similarity graphs; (ii) determining the number of clusters. Firstly, we study four similarity graphs to construct...
Accumulating evidence suggests that biological systems are composed of interacting, separable, functional modules-groups of vertices within which connections are dense but between which they are sparse. Identifying these modules is essential to understand the organization of biological systems. However, the most existing deterministic algorithms only find ldquodenserdquo clusters. Actually, the modules...
Accumulating evidence suggests that biological systems are composed of interacting, separable, functional modules which is that groups of vertices within which connections are dense but between which they are sparse. Identifying these modules is likely to capture the biologically meaningful interactions. In recent years, many algorithms have been developed for detecting such structures. These algorithms...
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