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Biomedical data mining is very important for the research of complex diseases, and disease-gene discovery is one of the most representative topics in this field. Multiscale module structure (MMS) that widely exists in biological networks can provide useful insight for disease research. However, how to effectively mine information in MMS to enhance the ability of disease-gene discovery is challenging. Thus, we propose a type of novel hybrid methods (HyMSMK) for disease-gene discovery by integrating multiscale module kernel (MSMK) derived from multiscale module profile (MSMP). We extract MSMP with local to global structural information from comprehensive human protein interactome by multiscale modularity optimization with exponential sampling, and construct MSMK by using the MSMP as a feature matrix, combining with the relative information content of features and kernel sparsification. Then, we present several fusion strategies integrating MSMK, including a probabilistic model for rank aggregation. By a series of experiments, we study the effect of the fusion strategies and kernel sparsification on HyMSMK, and demonstrate that HyMSMK outperforms the state-of-art network-based algorithms. These results confirm that MSMK is particularly helpful for disease-gene discovery, and the kernel sparsification can improve HyMSMK in storage space and computing speed. This may provide useful insights for the study and application of MMS.

Proteins drive virtually all cellular-level processes. The proteins that are critical to cell proliferation and survival are defined as essential. These essential proteins are implicated in key metabolic and regulatory networks, and are important in the context of rational drug design efforts. The computational identification of the essential proteins benefits from the proliferation of publicly available protein interaction datasets. Scientists have developed several algorithms that use these interaction datasets to predict essential proteins. However, a comprehensive web platform that facilitates the analysis and prediction of essential proteins is missing. In this study, we design, implement, and release NetEPD: a network-based essential protein discovery platform. This resource integrates data on Protein-Protein Interaction (PPI) networks, gene expression, subcellular localization, and a native set of essential proteins. It also computes a variety of node centrality measures, evaluates the predictions of essential proteins, and visualizes PPI networks. This comprehensive platform functions by implementing four activities, which include the collection of datasets, computation of centrality measures, evaluation, and visualization. The results produced by NetEPD are visualized on its website, and sent to a user-provided email, and they are available to download in a parsable format. This platform is freely available at http://bioinformatics.csu.edu.cn/netepd.

MotivationIdentifying disease-related genes is important for the study of human complex diseases. Module structures or community structures are ubiquitous in biological networks. Although the modular nature of human diseases can provide useful insights, the mining of information hidden in multiscale module structures has received less attention in disease-gene prediction.ResultsWe propose a hybrid method, HyMM, to predict disease-related genes more effectively by integrating the information from multiscale module structures. HyMM consists of three key steps: extraction of multiscale modules, gene rankings based on multiscale modules and integration of multiple gene rankings. The statistical analysis of multiscale modules extracted by three multiscale-module-decomposition algorithms (MO, AS and HC) shows that the functional consistency of the modules gradually improves as the resolution increases. This suggests the existence of different levels of functional relationships in the multiscale modules, which may help reveal disease-gene associations. We display the effectiveness of multiscale module information in the disease-gene prediction and confirm the excellent performance of HyMM by 5-fold cross-validation and independent test. Specifically, HyMM with MO can more effectively enhance the ability of disease-gene prediction; HyMM (MO, RWR) and HyMM (MO, RWRH) are especially preferred due to their excellent comprehensive performance, and HyMM (AS, RWRH) is also good choice due to its local performance. We anticipate that this work could provide useful insights for disease-module analysis and disease-gene prediction based on multi-scale module structures.Availabilityhttps://github.com/xiangiu0208/HvMMContactlimin@mail.csu.edu.cnSupplementary informationSupplementary data are available atBioinformaticsonline.

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.

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.

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.

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.