DCOAT: Dynamic Core-attachment based Protein Complex Detection

Identifying protein complexes from protein-protein interaction (PPI) networks is essential for understanding cellular organization and biological processes. Most of the existing works treat PPI networks as static and focus on detecting densely connected regions. Several recent works have explored the dynamics of PPIs in the network, but may identify complexes with false-positive edges of low confidence. In this paper, we propose a novel Dynamic COre-ATtachment based protein complex detection algorithm (DCOAT). First, we derive co-expressed gene biclusters from the gene expression data by a memetic algorithm to enable the construction of dynamic subnetworks from a static PPI network. Next, we evaluate the confidence of edges within each dynamic subnetwork by integrating both topological features and biological information to filter false-positive edges. Then, we detect complexes from each weighted subnetwork based on the core-attachment structure. Finally, we build an overlapping graph and merge redundant complexes using maximum weight matching. The experimental results validate that DCOAT achieves state-of-the-art performance compared to the nine baseline methods based on static and dynamic networks across multiple evaluation measures, especially in terms of F1 and precision. Moreover, the detected stable and temporal complexes are consistent with real biological scenarios and have biological significance. Our code and datasets are publicly available at https://github.com/LiGuojing194/DCOAT.