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Background
Inference of gene regulatory networks is a key goal in the quest for understanding fundamental cellular processes and revealing underlying relations among genes. With the availability of gene expression data, computational methods aiming at regulatory networks reconstruction are facing challenges posed by the data's high dimensionality, temporal dynamics or measurement noise. We propose an approach based on a novel multi-layer evolutionary trained neuro-fuzzy recurrent network (ENFRN) that is able to select potential regulators of target genes and describe their regulation type. 相似文献2.
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ABSTRACT: BACKGROUND: Dynamic Bayesian network (DBN) is among the mainstream approaches for modeling various biological networks, including the gene regulatory network (GRN). Most current methods for learning DBN employ either local search such as hill-climbing, or a meta stochastic global optimization framework such as genetic algorithm or simulated annealing, which are only able to locate sub-optimal solutions. Further, current DBN applications have essentially been limited to small sized networks. RESULTS: To overcome the above difficulties, we introduce here a deterministic global optimization based DBN approach for reverse engineering genetic networks from time course gene expression data. For such DBN models that consist only of inter time slice arcs, we show that there exists a polynomial time algorithm for learning the globally optimal network structure. The proposed approach, named GlobalMIT+, employs the recently proposed information theoretic scoring metric named mutual information test (MIT). GlobalMIT+ is able to learn high-order time delayed genetic interactions, which are common to most biological systems. Evaluation of the approach using both synthetic and real data sets, including a 733 cyanobacterial gene expression data set, shows significantly improved performance over other techniques. CONCLUSIONS: Our studies demonstrate that deterministic global optimization approaches can infer large scale genetic networks. 相似文献
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Xu R Wunsch Ii D Frank R 《IEEE/ACM transactions on computational biology and bioinformatics / IEEE, ACM》2007,4(4):681-692
Genetic regulatory network inference is critically important for revealing fundamental cellular processes, investigating gene functions, and understanding their relations. The availability of time series gene expression data makes it possible to investigate the gene activities of whole genomes, rather than those of only a pair of genes or among several genes. However, current computational methods do not sufficiently consider the temporal behavior of this type of data and lack the capability to capture the complex nonlinear system dynamics. We propose a recurrent neural network (RNN) and particle swarm optimization (PSO) approach to infer genetic regulatory networks from time series gene expression data. Under this framework, gene interaction is explained through a connection weight matrix. Based on the fact that the measured time points are limited and the assumption that the genetic networks are usually sparsely connected, we present a PSO-based search algorithm to unveil potential genetic network constructions that fit well with the time series data and explore possible gene interactions. Furthermore, PSO is used to train the RNN and determine the network parameters. Our approach has been applied to both synthetic and real data sets. The results demonstrate that the RNN/PSO can provide meaningful insights in understanding the nonlinear dynamics of the gene expression time series and revealing potential regulatory interactions between genes. 相似文献
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Although microarray data have been successfully used for gene clustering and classification, the use of time series microarray data for constructing gene regulatory networks remains a particularly difficult task. The challenge lies in reliably inferring regulatory relationships from datasets that normally possess a large number of genes and a limited number of time points. In addition to the numerical challenge, the enormous complexity and dynamic properties of gene expression regulation also impede the progress of inferring gene regulatory relationships. Based on the accepted model of the relationship between regulator and target genes, we developed a new approach for inferring gene regulatory relationships by combining target-target pattern recognition and examination of regulator-specific binding sites in the promoter regions of putative target genes. Pattern recognition was accomplished in two steps: A first algorithm was used to search for the genes that share expression profile similarities with known target genes (KTGs) of each investigated regulator. The selected genes were further filtered by examining for the presence of regulator-specific binding sites in their promoter regions. As we implemented our approach to 18 yeast regulator genes and their known target genes, we discovered 267 new regulatory relationships, among which 15% are rediscovered, experimentally validated ones. Of the discovered target genes, 36.1% have the same or similar functions to a KTG of the regulator. An even larger number of inferred genes fall in the biological context and regulatory scope of their regulators. Since the regulatory relationships are inferred from pattern recognition between target-target genes, the method we present is especially suitable for inferring gene regulatory relationships in which there is a time delay between the expression of regulating and target genes. 相似文献
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Computational inference and experimental validation of the nitrogen assimilation regulatory network in cyanobacterium Synechococcus sp. WH 8102 总被引:1,自引:0,他引:1
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Su Z Mao F Dam P Wu H Olman V Paulsen IT Palenik B Xu Y 《Nucleic acids research》2006,34(3):1050-1065
Deciphering the regulatory networks encoded in the genome of an organism represents one of the most interesting and challenging tasks in the post-genome sequencing era. As an example of this problem, we have predicted a detailed model for the nitrogen assimilation network in cyanobacterium Synechococcus sp. WH 8102 (WH8102) using a computational protocol based on comparative genomics analysis and mining experimental data from related organisms that are relatively well studied. This computational model is in excellent agreement with the microarray gene expression data collected under ammonium-rich versus nitrate-rich growth conditions, suggesting that our computational protocol is capable of predicting biological pathways/networks with high accuracy. We then refined the computational model using the microarray data, and proposed a new model for the nitrogen assimilation network in WH8102. An intriguing discovery from this study is that nitrogen assimilation affects the expression of many genes involved in photosynthesis, suggesting a tight coordination between nitrogen assimilation and photosynthesis processes. Moreover, for some of these genes, this coordination is probably mediated by NtcA through the canonical NtcA promoters in their regulatory regions. 相似文献
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