A Seed Expansion Graph Clustering Method for Protein Complexes Detection in Protein Interaction Networks

Abstract

Most proteins perform their biological functions while interacting as complexes. The detection of protein complexes is an important task not only for understanding the relationship between functions and structures of biological network, but also for predicting the function of unknown proteins. We present a new nodal metric by integrating its local topological information. The metric reflects its representability in a larger local neighborhood to a cluster of a protein interaction (PPI) network. Based on the metric, we propose a seed-expansion graph clustering algorithm (SEGC) for protein complexes detection in PPI networks. A roulette wheel strategy is used in the selection of the seed to enhance the diversity of clustering. For a candidate node u, we define its closeness to a cluster C, denoted as NC(u, C), by combing the density of a cluster C and the connection between a node u and C. In SEGC, a cluster which initially consists of only a seed node, is extended by adding nodes recursively from its neighbors according to the closeness, until all neighbors fail the process of expansion. We compare the F-measure and accuracy of the proposed SEGC algorithm with other algorithms on Saccharomyces cerevisiae protein interaction networks. The experimental results show that SEGC outperforms other algorithms under full coverage.

Enhanced Protein Complex Detection Using Square Clustering Coefficient

“…To assess the effectiveness of our proposed method, we compared it against several existing algorithms, namely HGCA (Wang et al, 2019), IPCA (Li et al, 2008), DCU (Zhao et al, 2014), and SEGC (Wang et al, 2017). In this comparative analysis, we specifically focused on methods that strive to encompass nearly 100 percent of the proteins when constructing complexes, ensuring that each node is included in at least one complex.…”

“…This recursive process ensures a comprehensive expansion of the cluster, guided by the local structural properties of the protein interaction network. SEGC, introduced in (Wang et al, 2017), presents a unique approach to seed selection by employing a roulette wheel strategy, thereby enhancing the diversity of clusters. Through the evaluation of both the cluster density and the connection of a node u to cluster C, the algorithm computes the closeness measure NC(u, C), indicating the proximity of the node to the cluster.…”

A Network Clustering Algorithm for Protein Complex Detection Fused with Power-Law Distribution Characteristic

“…To account for the minimum diameter and average node distance characteristics of protein complexes, the improved DPClus algorithm (IPCA) [17] enhances DPClus through the integration of sub-graph diameters and interaction probabilities, which provide insights into the density of the network. Other methods in this category include SEGC [18], Core [19], etc.…”

A self-adaptive and robust fission clustering algorithm via heat diffusion and maximal turning angle

“…There have been various different clustering methods in the literature. In general, they can be classified into 6 categories, that is density-based (c.f., DP [8], DP-HD [9], DBSCAN [10], NQ-DBSCAN [11], CSSub [12] and GDPC [13]), grid-based (c.f., CLIQUE [14], Gridwave [15] and WaveCluster [16]), model-based (c.f., Gaussian parsimonious [17], Gaussian mixture models [18] and Latent tree models [19]), partition-ing (c.f., K-means [20,21,22], K-partitioning [23] and TLBO [24]), graph-based (SEGC [25], ProClust [26] and MCSSGC [27]), and hierarchical (c.f., BIRCH [28] and CHAMELEON [29]) approaches.…”