BackgroundComprehensive analyzing multi-omics biological data in different conditions is important for understanding biological mechanism in system level. Multiple or multi-layer network model gives us a new insight into simultaneously analyzing these data, for instance, to identify conserved functional modules in multiple biological networks. However, because of the larger scale and more complicated structure of multiple networks than single network, how to accurate and efficient detect conserved functional biological modules remains a significant challenge.ResultsHere, we propose an efficient method, named ConMod, to discover conserved functional modules in multiple biological networks. We introduce two features to characterize multiple networks, thus all networks are compressed into two feature matrices. The module detection is only performed in the feature matrices by using multi-view non-negative matrix factorization (NMF), which is independent of the number of input networks. Experimental results on both synthetic and real biological networks demonstrate that our method is promising in identifying conserved modules in multiple networks since it improves the accuracy and efficiency comparing with state-of-the-art methods. Furthermore, applying ConMod to co-expression networks of different cancers, we find cancer shared gene modules, the majority of which have significantly functional implications, such as ribosome biogenesis and immune response. In addition, analyzing on brain tissue-specific protein interaction networks, we detect conserved modules related to nervous system development, mRNA processing, etc.ConclusionsConMod facilitates finding conserved modules in any number of networks with a low time and space complexity, thereby serve as a valuable tool for inference shared traits and biological functions of multiple biological system.Electronic supplementary materialThe online version of this article (10.1186/s12859-018-2434-5) contains supplementary material, which is available to authorized users.
Community detection in dynamic networks has been extensively studied since it sheds light on the structure-function relation of the overall complex systems. Recently, it has been demonstrated that the structural perturbation in static networks is excellent in characterizing the topology. In order to investigate the perturbation structural theory in dynamic networks, we extend the theory by considering the dynamic variation information between networks of consecutive time. Then a novel similarity is proposed by combing structural perturbation and topological features. Finally, we present an evolutionary clustering algorithm to detect dynamic communities under the temporal smoothness framework. Experimental results on both artificial and real dynamic networks demonstrate that the proposed similarity is promising in dynamic community detection since it improves the clustering accuracy compared with state-of-the-art methods, indicating the superiority of the presented similarity measure.
Water jet peening (WJP) is a mechanical surface strengthening process, which can improve the residual stress (RS) of the peened surface and then improve the fatigue life of components. In this paper, erosion experiments are conducted to investigate the influence of peening parameters on erosion. On this basis, RSs induced by WJP are studied in relation to the peening parameters. In addition, the coupled Eulerian–Lagrangian (CEL) technique is used to model and simulate the dynamic impact process of WJP on Al6061-T6. The influence of peening parameters such as jet pressure p, jet traverse velocity vf, and the number of water jet pass n on the modification of residual stress field (RSF) is examined by simulation and experiment. The influence of incidence angle α and water jet diameter d on RSF is also investigated by simulation. Results show that compressive RS σcrs is a result of the action of water-hammer pressure alone. Furthermore, σcrs increases with an increase in p, n and α. The optimal peening parameters for Al6061-T6 are found to be p = 60 MPa, vf = 2000 mm/min, n = 4, α = 90 deg and d = 2.0 mm. Finally, the depth of compressive RS layer D0 increases greatly with an increase in water jet diameter d and can reach 984 μm.
Determining the dynamics of pathways associated with cancer progression is critical for understanding the etiology of diseases. Advances in biological technology have facilitated the simultaneous genomic profiling of multiple patients at different clinical stages, thus generating the dynamic genomic data for cancers. Such data provide enable investigation of the dynamics of related pathways. However, methods for integrative analysis of dynamic genomic data are inadequate. In this study, we develop a novel nonnegative matrix factorization algorithm for dynamic modules ( NMF-DM), which simultaneously analyzes multiple networks for the identification of stage-specific and dynamic modules. NMF-DM applies the temporal smoothness framework by balancing the networks at the current stage and the previous stage. Experimental results indicate that the NMF-DM algorithm is more accurate than the state-of-the-art methods in artificial dynamic networks. In breast cancer networks, NMF-DM reveals the dynamic modules that are important for cancer stage transitions. Furthermore, the stage-specific and dynamic modules have distinct topological and biochemical properties. Finally, we demonstrate that the stage-specific modules significantly improve the accuracy of cancer stage prediction. The proposed algorithm provides an effective way to explore the time-dependent cancer genomic data.
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