Differential network analysis is essential for revealing patterns of network rewiring across various conditions and understanding the biological mechanisms underlying complex diseases. Existing methods often rely on limited modalities and do not adequately capture temporal dynamics in gene expression. To address these limitations, we propose a novel framework for constructing multiple dynamic differential protein interaction networks, named Multi-DPIN, which combines temporal gene expression data with protein-protein interaction topology. Initially, active proteins and their interactions are identified using the 3 -sigma rule to construct dynamic disease networks and background networks. Subsequently, common topological structures shared with the background network are eliminated from each disease network. Finally, consistent structures across all disease networks are identified to establish the multiple dynamic differential protein interaction networks. We evaluated Multi-DPIN on publicly available cancer datasets (breast cancer and acute myeloid leukemia) and compared it with five baseline methods. Experimental results demonstrate that Multi-DPIN outperforms existing methods in identifying known oncogenes.

miRNAs (microRNAs) play a significant role in the occurrence, development and treatment of diseases. Many computational algorithms for identifying disease-related miRNAs have been proposed due to high cost of biological experiments, while most of them ignore the existence of higherorder interactions in biological networks, limiting the improvement of performance. Therefore, we proposed a novel algorithm for identifying miRNA-disease associations by multiple kernel learning with higher-order interactions (HMKLMDA). First, we construct a kind of new target kernels for fusion of multiple similarity kernels for diseases and miRNAs by considering higher-order interactions of profiles, and then we design a kind of kernel-fusion strategy by integrating high-order kernel combinations. Finally, the potential miRNA-disease associations are identified based on the fused kernels for diseases and miRNAs by Laplacian regularized least squares with graph regularization and parameter constraints. By a series of experimental evaluations, we investigated the effect of different parameters and the contributions of higher-order interactions and then demonstrated superior performance of our algorithm in identifying miRNA-disease associations.

Detecting protein complexes holds paramount importance in elucidating cellular organization and protein functionalities. Over the past decade, numerous approaches have centered their attention on the topological intricacies of protein-protein interaction (PPI) networks, yet these have often fallen short in harnessing the full spectrum of biological information inherent in proteins. To bridge this gap, we introduce a novel methodology, designated NE-DPC, which integrates both the topological landscape of PPI networks and the attribute profiles of proteins to facilitate the identification of protein complexes. Initially, we fuse the static PPI network with protein attributes through advanced network embedding techniques. Subsequently, we construct a cosine similarity matrix grounded on these embedded vectors, capturing the intricate relationships among proteins. Lastly, we employ a core-attachment strategy to pinpoint protein complexes. The results reveal that NE-DPC outperforms cutting-edge methods, demonstrating its effectiveness and potential in advancing protein complex detection.

Identifying disease-associated genes (DAGs) is important for the research of complex diseases, and network-based methods have been a powerful and elegant strategy for this topic. Genes and their products perform biological functions through synergy in biological networks, but mining useful information from the networks remains an open issue. Therefore, we propose a novel heterogeneous network impulsive dynamics model to identify DAGs more effectively. It inspires a heterogeneous network impulsive dynamical process by imposing impulsive signals at specific nodes, in an enhanced dual-layer heterogeneous network. Then, it extracts the impulsive dynamical signatures of responses of nodes to the impulsive signals so as to infer DAGs. A series of experiments confirm that this model has good performance of inferring DAGs under different conditions, and case studies further demonstrate its effectiveness. Furthermore, a user-friendly web platform is provided to facilitate prioritization and analysis of DAGs. It may become a useful tool for studying complex diseases and relevant genes.

Identification of protein complex is an important issue in the field of system biology, which is crucial to understanding the cellular organization and inferring protein functions. Recently, many computational methods have been proposed to detect protein complexes from protein-protein interaction (PPI) networks. However, most of these methods only focus on local information of proteins in the PPI network, which are easily affected by the noise in the PPI network. Meanwhile, it's still challenging to detect protein complexes, especially for overlapping cases. To address these issues, we propose a new method, named Dopcc, to detect overlapping protein complexes by constructing a multi-metrics network according to different strategies. First, we adopt the Jaccard coefficient to measure the neighbor similarity between proteins and denoise the PPI network. Then, we propose a new strategy, integrating hierarchical compressing with network embedding, to capture the high-order structural similarity between proteins. Further, a new co-core attachment strategy is proposed to detect overlapping protein complexes from multi-metrics. The experimental results show that our proposed method, Dopcc, outperforms the other eight state-of-the-art methods in terms of F-measure, MMR, and Composite Score on two yeast datasets.

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.

MOTIVATION: Identifying disease-related genes is an important issue in computational biology. Module structure widely exists in biomolecule networks, and complex diseases are usually thought to be caused by perturbations of local neighborhoods in the networks, which can provide useful insights for the study of disease-related genes. However, the mining and effective utilization of the module structure is still challenging in such issues as a disease gene prediction. RESULTS: We propose a hybrid disease-gene prediction method integrating multiscale module structure (HyMM), which can utilize multiscale information from local to global structure to more effectively predict disease-related genes. HyMM extracts module partitions from local to global scales by multiscale modularity optimization with exponential sampling, and estimates the disease relatedness of genes in partitions by the abundance of disease-related genes within modules. Then, a probabilistic model for integration of gene rankings is designed in order to integrate multiple predictions derived from multiscale module partitions and network propagation, and a parameter estimation strategy based on functional information is proposed to further enhance HyMM's predictive power. By a series of experiments, we reveal the importance of module partitions at different scales, and verify the stable and good performance of HyMM compared with eight other state-of-the-arts and its further performance improvement derived from the parameter estimation. CONCLUSIONS: The results confirm that HyMM is an effective framework for integrating multiscale module structure to enhance the ability to predict disease-related genes, which may provide useful insights for the study of the multiscale module structure and its application in such issues as a disease-gene prediction.

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.

Cell heterogeneity analysis is an important and urgent task in single cell data research. Numerous cell type identification methods have been proposed to address the issue. Due to the high rate of dropout and complex biological background, it is still a challenging task to obtain the accurate clusters of cells. In this study, we propose a robust single cell clustering method based on subspace learning and partial imputation, called RCSLI. RCSLI incorporates a modified variable genes selection method and utilizes the self-expression of scRNA-seq data to learn sparse cell-to-cell similarity and impute part of missing expression values. To evaluate the clustering performance of RCSLI, we compare it with nine state-of-the-art single cell clustering methods on eight scRNA-seq datasets. The experimental results show that RCSLI gets more accurate and robust clustering results. The imputation impact on the specific gene markers is evaluated on PBMC data. The classification results by taking these marker genes as predictors show RCSLI recovers the real dropouts, meanwhile, introduces less noise.

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.

Detecting protein complexes from protein-protein interaction (PPI) networks provides biologists an opportunity to efficiently understand the cellular organizations and functions. Existing computational methods just focus on mining high-density regions as the protein complexes by searching the local topological information of a PPI network and ignore the global topological information. To address this limitation, in this study, we present a novel protein complex detection method based on hierarchical compressing network embedding, named DPC-HCNE. The proposed method can preserve both the local topological information and global topological information of a PPI network. To evaluate the performance of our method, DPC-HCNE is compared with other eight typical clustering algorithms to detect protein complexes on two yeast datasets. The experimental results show that DPC-HCNE outperforms those state-of-the-art complex detection methods.

With the advances in high-throughput technology, a large number of protein interactions data have been burgeoning in recent years, which makes it possible for considering dynamic properties of protein interaction networks(PINs) instead of static properties. To address the limitation of the existing dynamic PIN analysis approaches, in this paper, we proposed 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 was adopted to identify protein complexes from ST-APIN and other three dynamic PINs, NF-APIN, DPIN, TC-PIN. The experimental results showed that, the performance of MCL on ST-APIN outperforms those on the three other dynamic networks in terms of matching with known complexes, sensitivity, specificity and f-measure. Furthermore, we evaluated the identified protein complexes by GO (Gene Ontology) function enrichment analysis. The validation showed that the identified protein complexes from ST-APIN were more biologically significance. This study provided a general paradigm for constructing the ST-APINs, which can be used for theoretical studies and clinic applications.