Background: Inferring a gene regulatory network (GRN) from high throughput biological data is often an under-determined problem and is a challenging task due to the following reasons: (1) thousands of genes are involv...
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Background: Inferring a gene regulatory network (GRN) from high throughput biological data is often an under-determined problem and is a challenging task due to the following reasons: (1) thousands of genes are involved in one living cell;(2) complex dynamic and nonlinear relationships exist among genes;(3) a substantial amount of noise is involved in the data, and (4) the typical small sample size is very small compared to the number of genes. We hypothesize we can enhance our understanding of gene interactions in important biological processes (differentiation, cell cycle, and development, etc) and improve the inference accuracy of a GRN by (1) incorporating prior biological knowledge into the inference scheme, (2) integrating multiple biological data sources, and (3) decomposing the inference problem into smaller network modules. Results: This study presents a novel GRN inference method by integrating gene expression data and gene functional category information. The inference is based on module network model that consists of two parts: the module selection part and the network inference part. The former determines the optimal modules through fuzzy c-mean (FCM) clustering and by incorporating gene functional category information, while the latter uses a hybrid of particleswarmoptimization and recurrent neural network (PSO-RNN) methods to infer the underlying network between modules. Our method is tested on real data from two studies: the development of rat central nervous system (CNS) and the yeast cell cycle process. The results are evaluated by comparing them to previously published results and gene ontology annotation information. Conclusion: The reverse engineering of GRNs in time course gene expression data is a major obstacle in system biology due to the limited number of time points. Our experiments demonstrate that the proposed method can address this challenge by: (1) preprocessing gene expression data (e. g. normalization and missing value imputation)
Several improvements about basic particleswarmoptimization (PSO) algorithm has been presented. In the improved particleswarmoptimization (IPSO) algorithm, the particles are initialized with chaos
Several improvements about basic particleswarmoptimization (PSO) algorithm has been presented. In the improved particleswarmoptimization (IPSO) algorithm, the particles are initialized with chaos
Bio-inspired evolutionary algorithms are probabilistic search methods that simulate the natural biological evolution or the behaviour of biological entities. Such algorithms can be used to obtain near optimal solution...
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ISBN:
(纸本)9781424450534
Bio-inspired evolutionary algorithms are probabilistic search methods that simulate the natural biological evolution or the behaviour of biological entities. Such algorithms can be used to obtain near optimal solutions in optimization problems, for which traditional mathematical techniques may fail. This paper does a comparative study of results of five evolutionary algorithms: Genetic algorithm (GA), particleswarmoptimization (PSO) algorithm, Artificial Bee Colony (ABC) algorithm, Invasive Weed optimization (IWO) algorithm and Artificial Immune (AI) algorithm when applied to some standard benchmark multivariable functions.
Generally, Hardware/Software (HW/SW) partitioning can be approximately resolved through some kinds of optimal algorithms. Based oil both characteristics of HW/SW partitioning and particleswarmoptimization (PSO) algo...
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ISBN:
(纸本)9783642030949
Generally, Hardware/Software (HW/SW) partitioning can be approximately resolved through some kinds of optimal algorithms. Based oil both characteristics of HW/SW partitioning and particleswarmoptimization (PSO) algorithm, a novel parallel FlW/SW partitioning method is proposed in this paper. A model of parallel HW/SW partitioning on the basis of PSO algorithm is established after analyzing the particularity of HW/SW partitioning. A hybrid strategy of PSO and Tabu Search (TS) is proposed in this paper, which uses the intrinsic parallelism of PSO and the memory function of TS to speed tip and improve the performance of PSO. To settle the problem of premature convergence, the reproduction and crossover operation of genetic algorithm (GA) is also introduced into procedure of PSO. Experimental results indicate that the parallel PSO algorithm can efficiently reduce the running time even for large task graphs.
This paper presents a comprehensive study of forecasting a day-ahead of load and locational marginal pricing (LMP) using artificial intelligent systems. An artificial neural network (ANN) is trained with a stochastic ...
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ISBN:
(纸本)9781424435098
This paper presents a comprehensive study of forecasting a day-ahead of load and locational marginal pricing (LMP) using artificial intelligent systems. An artificial neural network (ANN) is trained with a stochastic optimization technique called particleswarmoptimization (PSO). This training algorithm works to adjust the network weights and biases as to minimize the error function. Wavelet transformed data is fed into neural network as preprocessing stage in order to get a better price pattern that will be reliable for forecasting. The proposed models were trained and tested using real data consists of historical load and LMP and corresponding influence variables such as weather information and marginal losses cost (MLC). The data used is from NYISO and Weather Source Stations, Buffalo, New York over a period of three years (2001-2003). Simulation results are compared with that of conventional back-propagation (BP) neural network and radial basis function network (RBFN) and provided highly accurate generalization capability.
Intrusion detection plays more important role in network security today. This paper introduces a method, particleswarmoptimization and support vector machine, to intrusion detection system, and presents a new design...
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ISBN:
(纸本)9781424447053
Intrusion detection plays more important role in network security today. This paper introduces a method, particleswarmoptimization and support vector machine, to intrusion detection system, and presents a new design of ID Based on particleswarmoptimization and Support Vector Machine. This paper presents an optimal selection approach of the SVM parameters(regulation parameter C and the radial basis function width parameter sigma) based on particle swarm optimization algorithm. The experiments show that the optimal parameter selection approach based on PSO is available and the Research of Intrusion Detection Based on particleswarmoptimization and Support Vector Machine is effective in reducing the number of alerts, false positive, false negative better.
In order to detect a weak signal under the condition of intensive noise, the signal and additive white noise were used as input of a bistable stochastic resonance (SR) system. The noise intensity and the system parame...
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ISBN:
(纸本)9781424427994
In order to detect a weak signal under the condition of intensive noise, the signal and additive white noise were used as input of a bistable stochastic resonance (SR) system. The noise intensity and the system parameters were adjusted adaptively with particleswarmoptimization (PSO) algorithm by examining the SR effect on output signal-to-noise ratio (SNR). An improved numerical solution for a bistable SR model based on a fourth order Runge-Kutta algorithm was presented to enhance the SR effect. The simulation results show that the weak signal in an intensive noisy background could be successfully extracted. What is more, the output SNR was increased more than 20 dB comparing with the input SNR. The proposed approach was used to process the vibration signals of roller bearings to find the small faults in an early stage. The result showed that the approach satisfactorily extracts the defect characteristics. It can be seen that the proposed method was superior to the traditional spectra analysis and wavelet transform methods. Such detection approach indicates a promising prospect for mechanical fault monitoring and diagnosis.
To resolve the problem of traditional lifetime, target coverage and network connectivity, a novel algorithm for selecting the optimal coverage set based on improved particle swarm optimization algorithm (PSOA) is prop...
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ISBN:
(纸本)9780769539010
To resolve the problem of traditional lifetime, target coverage and network connectivity, a novel algorithm for selecting the optimal coverage set based on improved particle swarm optimization algorithm (PSOA) is proposed. There are two competing objectives presented to determine where to place the sensor nodes, the coverage rate and the number of working nodes. And then As another new contribution, we apply the novel algorithm in the K-disjoint coverage sets problem, which divides all the sensors into K-disjoint sets, guaranteeing each set with complete coverage. This method can improve the capability of search and convergence of algorithm. By alternating coverage subsets and using only one at each round, the maximum network lifetime is achieved. The simulation result shows that our analysis for wireless sensor networks is better than other algorithms and more effective.
This paper presents an approach based on idle time windows (ITWs) and particleswarmoptimization (PSO) algorithm to solve dynamic scheduling of multi-task for hybrid flow-shop. The idea of ITW is introduced, then the...
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ISBN:
(纸本)9781424427239
This paper presents an approach based on idle time windows (ITWs) and particleswarmoptimization (PSO) algorithm to solve dynamic scheduling of multi-task for hybrid flow-shop. The idea of ITW is introduced, then the dynamic updating rules of the sets of ITWs are explained in detail. With the sets of ITWs of machines as constraints, the mathematical model is presented for dynamic scheduling of multi-task for hybrid flow-shop. The PSO algorithm is proposed in order to solve this problem. The results of simulation indicate that this approach satisfies the demand of dynamic scheduling of multi-task.
In the predicting financial distress, we know that irrelevant or correlated features in the samples could spoil the performance of the SVR classifier, leading to decrease of prediction accuracy. In order to solve the ...
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ISBN:
(纸本)9780769538808
In the predicting financial distress, we know that irrelevant or correlated features in the samples could spoil the performance of the SVR classifier, leading to decrease of prediction accuracy. In order to solve the problems mentioned above, this paper use rough sets as a preprocessor of SVR to select a subset of input variables and employ the particle swarm optimization algorithm (PSOA) to optimize the parameters of SVR. The proposed PSOA-SVR model can automatically determine the optimal parameters. This model is tested on the prediction of financial distress. Then, we compare the proposed PSOA -SVR model with other artificial intelligence models of (BPN and fix-SVR). The experiment indicates that the proposed method is quite effective and ubiquitous.
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