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The finite-time passivity and finite-time synchronization issues for two categories of coupled complex-valued memristive neural networks with and without time-varying delay are discussed. First, by selecting a suitable control scheme and exploiting some inequality techniques, several finite-time passivity conditions are proposed for coupled complex-valued memristive neural networks without time-varying delay. Based on the obtained finite-time passivity condition, a criterion for finite-time synchronization for the considered network is also established. Furthermore, for coupled complex-valued memristive neural networks with time-varying delay, some criteria are established to guarantee that the considered network is finite-time passive and finite-time synchronized by developing an appropriate controller and deploying the Lyapunov functional method. Finally, two numerical examples with simulations substantiate the correctness and validity of the results. [ABSTRACT FROM AUTHOR]

This paper studies the problem of event-triggered state estimation for complex network systems with quantization. The event-triggered communication scheme and quantization are employed to reduce the burden of network transmission. By utilizing Lyapunov stability theory and linear matrix inequality techniques, sufficient conditions are established which can ensure the augmented estimation error system to be asymptotically stable. Furthermore, the explicit expressions of the desired state estimators are derived in terms of linear matrix inequalities. Finally, an example is provided to illustrate the usefulness of the proposed method. [ABSTRACT FROM AUTHOR]

Markov clustering (MCL) is a commonly used algorithm for clustering networks in bioinformatics. It shows good performance in clustering dynamic protein–protein interaction networks (DPINs). However, a limitation of MCL and its variants (e.g, regularized MCL and soft regularized MCL) is that the clustering results are mostly dependent on the parameters whose values are user-specified. In this study, we propose a new MCL method based on the firefly algorithm (FA) to identify protein complexes from DPIN. Based on three-sigma principle, we construct the DPIN and discuss an overall modeling process. In order to optimize parameters, we exploit a number of population-based optimization methods. A thorough comparison completed for different swarm optimization algorithms such as particle swarm optimization (PSO) and firefly algorithm (FA) has been carried out. The identified protein complexes on the DIP dataset show that the new algorithm outperforms the state-of-the-art approaches in terms of accuracy of protein complex identification. [ABSTRACT FROM AUTHOR]

Abstract: This paper investigates the robust dynamic output feedback control problem for networked control systems (NCSs) with quantized measurements. The measurement losses of the communicated information are considered in an unreliable communication channel. The robust dynamic output feedback controllers are designed to handle the measurement losses and mitigate the quantization effects such that the resultant closed-loop NCS is mean-square stochastically stable with a prescribed disturbance attenuation performance. The controller existence conditions can be derived in terms of linear matrix inequalities (LMIs). Finally, an example is provided to illustrate the effectiveness of the proposed approach. [Copyright &y& Elsevier]

More and more studies have demonstrated that circRNAs can be used as markers of various diseases due to their inherent stability. Numerous computational methods, especially artificial intelligence approaches, have been applied to the prediction of circRNA-disease associations. However, the objective functions of these prediction methods are usually single and standard. The single objective function hardly reflects the characteristics of the prediction problem, and leads to low prediction accuracy. There has never been a method to design a set of objective functions for the circRNA-disease prediction problem and solve it by using intelligent optimization algorithms. In this paper, a method ICDMOE is proposed to identify circRNA-disease associations via a multi-objective evolutionary algorithm. A solution space is defined and four objective functions are designed based on matrix factorization and modularity of similarity networks. An improved decomposition-based multi-objective evolutionary algorithm (MOEA/D) is also employed to solve these objective functions. In the MOEA/D, an adaptive penalty-based boundary intersection strategy is proposed, which not only ensures convergence, but also guarantees the diversity of solutions in the Pareto front. Finally, the experimental results show that ICDMOE has better performance than pure matrix factorization-based methods, non-adaptive MOEA/D-based methods and other prediction methods. Furthermore, we find that the predicted disease-circRNAs can be confirmed by existing studies, miRNA regulations and expression profiles, etc. These indicate that ICDMOE can provide good candidates for biomedical experiments. [ABSTRACT FROM AUTHOR]

Protein complexes are very important for investigating the characteristics of biological processes. Identifying protein complexes from protein–protein interaction (PPI) networks is one of the recent research endeavors. The critical step of the seed-growth algorithms used for identifying protein complexes from PPI networks is to detect seed nodes (proteins) from which protein complexes are growing up in PPI networks. Topology potential was proposed to understand the evolution behavior and organizational principles of complex networks such as PPI networks. Furthermore, PPI networks are inherently dynamic in nature. In this study, we proposed a new seed-growing algorithm (called TP-WDPIN) for identifying protein complexes, which employs the concept of topology potential to detect significant proteins and mine protein complexes from Weighted Dynamic PPI Networks. To investigate the performance of the method, the TP-WDPIN algorithm was applied to four PPI databases and compared the obtained results to those produced by six other competing algorithms. Experimental results have demonstrated that the proposed TP-WDPIN algorithm exhibits better performance than other methods such as MCODE, MCL, CORE, CSO, ClusterONE, COACH when experimenting with four PPI databases (DIP, Krogan, MIPS, Gavin). [ABSTRACT FROM AUTHOR]

Nonnegative matrix factorization (NMF) has recently become a very popular unsupervised learning method because of its representational properties of factors and simple multiplicative update algorithms for solving the NMF. However, for the common NMF approach of minimizing the Euclidean distance between approximate and true values, the convergence of multiplicative update algorithms has not been well resolved. This paper first discusses the convergence of existing multiplicative update algorithms. We then propose a new multiplicative update algorithm for minimizing the Euclidean distance between approximate and true values. Based on the optimization principle and the auxiliary function method, we prove that our new algorithm not only converges to a stationary point, but also does faster than existing ones. To verify our theoretical results, the experiments on three data sets have been conducted by comparing our proposed algorithm with other existing methods. [ABSTRACT FROM AUTHOR]