Biological functions of a cell are typically carried out through protein complexes. The detection of protein complexes is therefore of great significance for understanding the cellular organizations and protein functions. In the past decades, many computational methods have been proposed to detect protein complexes. However, most of the existing methods just search the local topological information to mine dense subgraphs as protein complexes, ignoring the global topological information. To tackle this issue, we propose the DPCMNE method to detect protein complexes via multi-level network embedding. It can preserve both the local and global topological information of biological networks. First, DPCMNE employs a hierarchical compressing strategy to recursively compress the input protein-protein interaction (PPI) network into multi-level smaller PPI networks. Then, a network embedding method is applied on these smaller PPI networks to learn protein embeddings of different levels of granularity. The embeddings learned from all the compressed PPI networks are concatenated to represent the final protein embeddings of the original input PPI network. Finally, a core-attachment based strategy is adopted to detect protein complexes in the weighted PPI network constructed by the pairwise similarity of protein embeddings. To assess the efficiency of our proposed method, DPCMNE is compared with other eight clustering algorithms on two yeast datasets. The experimental results show that the performance of DPCMNE outperforms those state-of-the-art complex detection methods in terms of F1 and F1 + Acc . Furthermore, the results of functional enrichment analysis indicate that protein complexes detected by DPCMNE are more biologically significant in terms of P-score .

The rapid development of proteomics and high-throughput technologies has produced a large amount of Protein-Protein Interaction (PPI) data, which makes it possible for considering dynamic properties of protein interaction networks (PINs) instead of static properties. Identification of protein complexes from dynamic PINs becomes a vital scientific problem for understanding cellular life in the post genome era. Up to now, plenty of models or methods have been proposed for the construction of dynamic PINs to identify protein complexes. However, most of the constructed dynamic PINs just focus on the temporal dynamic information and thus overlook the spatial dynamic information of the complex biological systems. To address the limitation of the existing dynamic PIN analysis approaches, in this paper, we propose a new model-based scheme for the construction of the Spatial and Temporal Active Protein Interaction Network (ST-APIN) by integrating time-course gene expression data and subcellular location information. To evaluate the efficiency of ST-APIN, the commonly used classical clustering algorithm MCL is adopted to identify protein complexes from ST-APIN and the other three dynamic PINs, NF-APIN, DPIN, and TC-PIN. The experimental results show that, the performance of MCL on ST-APIN outperforms those on the other three dynamic PINs in terms of matching with known complexes, sensitivity, specificity, and f-measure. Furthermore, we evaluate the identified protein complexes by Gene Ontology (GO) function enrichment analysis. The validation shows that the identified protein complexes from ST-APIN are more biologically significant. This study provides a general paradigm for constructing the ST-APINs, which is essential for further understanding of molecular systems and the biomedical mechanism of complex diseases.

Hubs are generally defined as nodes with a high degree centrality, and they are important for maintaining the stability of complex networks. Previous studies have shown that hub proteins tend to be essential in protein-protein interaction (PPI) networks, providing us with a new way to analyze the essentiality of proteins. Unfortunately, most of the existing studies leverage static PPI networks that are both incomplete and noisy and ignore the temporal and spatial characteristics of PPI networks. Benefiting from the development of high-throughput technologies, abundant multi-biological datasets have been accumulated and can be used for network analysis. To reexamine the relationship between the network centrality and protein essentiality in PPI networks, in this study, we integrated PPI networks with gene expression data and subcellular localization information to construct temporal-spatial dynamic PPI networks. Based on the constructed temporal-spatial dynamic PPI networks, we introduced the maximum degree centrality (MDC) method to evaluate the essentiality of hub proteins. Our results illustrate that the integration of gene expression data or subcellular localization information can significantly reduce noise effects and improve the identification accuracy of essential proteins through the temporal-spatial analysis with disparate sources of PPI networks. Moreover, we redefined hubs and classified them into two types: temporospatial hubs and static hubs. The results show that temporospatial hub proteins are more likely to be essential.

Protein complexes play an essential role in living cells. Detecting protein complexes is crucial to understand protein functions and treat complex diseases. Due to high time and resource consumption of experiment approaches, many computational approaches have been proposed to detect protein complexes. However, most of them are only based on protein-protein interaction (PPI) networks, which heavily suffer from the noise in PPI networks. Therefore, we propose a novel core-attachment method, named CACO, to detect human protein complexes, by integrating the functional information from other species via protein ortholog relations. First, CACO constructs a cross-species ortholog relation matrix and transfers GO terms from other species as a reference to evaluate the confidence of PPIs. Then, a PPI filter strategy is adopted to clean the PPI network and thus a weighted clean PPI network is constructed. Finally, a new effective core-attachment algorithm is proposed to detect protein complexes from the weighted PPI network. Compared to other thirteen state-of-the-art methods, CACO outperforms all of them in terms of F-measure and Composite Score, showing that integrating ortholog information and the proposed core-attachment algorithm are effective in detecting protein complexes. CACO is available at https://csuligroup.com/CACO/ and https://github.com/CSUBioGroup/CACO .

Drug repositioning has been widely applied to explore new therapeutic applications for existing drugs, significantly reducing the transition time from laboratory research to clinical application. However, most existing models rely on static training over complex and large-scale networks, lacking detailed analysis and specificity for individual drug-disease pairs. To address this limitation, we propose the subgraph-aware graph attention network (SAGAN), which constructs subgraphs centered on target drug-disease pairs by extracting their surrounding interactions. Within each extracted subgraph, SAGAN employs an attention mechanism to focus on critical interactions among directly associated nodes within the subgraph, while simultaneously capturing cross-level relational patterns. A hierarchical pooling technique is then applied to aggregate the nodes and edges within the subgraph into more compact representations. Additionally, SAGAN integrates neighborhood features and interaction information from drug and disease similarity networks to enhance the expressive power of subgraph features. Finally, the method predicts drug-disease associations as a graph classification task. On three widely used benchmark datasets, SAGAN demonstrates outstanding performance, achieving an average area under the receiver operating characteristic curve (AUROC) of 0.9648 and an average area under the precision-recall curve (AUPR) of 0.9678, showcasing its robustness in handling sparse and imbalanced data. Furthermore, case studies validate the practical utility of SAGAN in predicting potential effective drugs for Alzheimer’s disease and breast cancer.

Leukemia is a malignant disease characterized by progressive accumulation with high morbidity and mortality rates, and investigating its disease genes is crucial for understanding its etiology and pathogenesis. Network propagation methods have emerged and been widely employed in disease gene prediction, but most of them focus on static biological networks, which hinders their applicability and effectiveness in the study of progressive diseases. Moreover, there is currently a lack of special algorithms for the identification of leukemia disease genes. Here, we proposed a novel Dynamic Network-based model integrating Differentially expressed Genes (DyNDG) to identify leukemia-related genes. Initially, we constructed a time-series dynamic network to model the development trajectory of leukemia. Then, we built a background–temporal multilayer network by integrating both the dynamic network and the static background network, which was initialized with differentially expressed genes at each stage. To quantify the associations between genes and leukemia, we extended a random walk process to the background–temporal multilayer network. The results demonstrate that DyNDG achieves superior accuracy compared to several state-of-the-art methods. Moreover, after excluding housekeeping genes, DyNDG yields a set of promising candidate genes associated with leukemia progression or potential biomarkers, indicating the value of dynamic network information in identifying leukemia-related genes. The implementation of DyNDG is available at both https://ngdc.cncb.ac.cn/biocode/tool/BT7617 and https://github.com/CSUBioGroup/DyNDG.