One of the important goals in systems biology is to infer transcription network based on gene expression data. Validation of the reconstructed network often requires benchmark datasets, e.g. gene expression data, whic...
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One of the important goals in systems biology is to infer transcription network based on gene expression data. Validation of the reconstructed network often requires benchmark datasets, e.g. gene expression data, which are usually unattainable. Synthetic datasets are therefore often needed to test the structure learning algorithms in a fast and reproducible manner. However, due to the lack of knowledge about the gene expression profiles, synthetic datasets may not resemble the biological reality. Here we present a computational tool, namely, ReTRN (Real Transcriptional Regulatory Networks) for extracting subnetworks from known transcription network and for generating corresponding gene expression data. By comparing with other implementations, we demonstrate that the network generated by ReTRN possesses scale free property, which resembles the biological reality. Moreover, ReTRN simultaneously generates gene expression data reflecting the temporal relationship in gene expression. We conclude that ReTRN provides a valid alternative to existing implementation and can be widely used in systems biology research. (c) 2009 Elsevier Inc. All rights reserved.
This work contributes to develop a new methodology to identify empirical-data-driven causal structure of a domain knowledge. We propose an algorithm as a two-stage procedure by first drawing relevant prior partial rel...
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This work contributes to develop a new methodology to identify empirical-data-driven causal structure of a domain knowledge. We propose an algorithm as a two-stage procedure by first drawing relevant prior partial relationships between variables and using them as structure constraints in a structurelearning task of Bayesian networks (BNs). The latter is then based on a model averaging approach to obtain a statistically sound BN. The empirical study focuses on modeling commuters' travel mode choice. We present experimental results on testing the design of prior restrictions, the effect of resampling size and learningalgorithms, and the effect of random draw on fitted BN structure. The results show that the proposed method can capture more sophisticated relationships between the variables that are missing in both decision tree models and random utility models.
Background: Dynamic Bayesian Network (DBN) is an approach widely used for reconstruction of gene regulatory networks from time-series microarray data. Its performance in network reconstruction depends on a structure l...
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Background: Dynamic Bayesian Network (DBN) is an approach widely used for reconstruction of gene regulatory networks from time-series microarray data. Its performance in network reconstruction depends on a structure learning algorithm. REVEAL (REVerse Engineering algorithm) is one of the algorithms implemented for learning DBN structure and used to reconstruct gene regulatory networks (GRN). However, the two-stage temporal Bayes network (2TBN) structure of DBN that specifies correlation between time slices cannot be obtained by score metrics used in REVEAL. Methods: In this paper, we study a more sophisticated score function for DBN first proposed by Nir Friedman for stationary DBNs structurelearning of both initial and transition networks but has not yet been used for reconstruction of GRNs. We implemented Friedman's Bayesian Information Criterion (BIC) score function, modified K2 algorithm to learn Dynamic Bayesian Network structure with the score function and tested the performance of the algorithm for GRN reconstruction with synthetic time series gene expression data generated by GeneNetWeaver and real yeast benchmark experiment data. Results: We implemented an algorithm for DBN structurelearning with Friedman's score function, tested it on reconstruction of both synthetic networks and real yeast networks and compared it with REVEAL in the absence or presence of preprocessed network generated by Zou&Conzen's algorithm. By introducing a stationary correlation between two consecutive time slices, Friedman's score function showed a higher precision and recall than the naive REVEAL algorithm. Conclusions: Friedman's score metrics for DBN can be used to reconstruct transition networks and has a great potential to improve the accuracy of gene regulatory network structure prediction with time series gene expression datasets.
In light of the low signal-to-noise nature of many large biological data sets, we propose a novel method to learn the structure of association networks using Gaussian graphical models combined with prior knowledge. Ou...
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In light of the low signal-to-noise nature of many large biological data sets, we propose a novel method to learn the structure of association networks using Gaussian graphical models combined with prior knowledge. Our strategy includes two parts. In the first part, we propose a model selection criterion called structural Bayesian information criterion, in which the prior structure is modeled and incorporated into Bayesian information criterion. It is shown that the popular extended Bayesian information criterion is a special case of structural Bayesian information criterion. In the second part, we propose a two-step algorithm to construct the candidate model pool. The algorithm is data-driven and the prior structure is embedded into the candidate model automatically. Theoretical investigation shows that under some mild conditions structural Bayesian information criterion is a consistent model selection criterion for high-dimensional Gaussian graphical model. Simulation studies validate the superiority of the proposed algorithm over the existing ones and show the robustness to the model misspecification. Application to relative concentration data from infant feces collected from subjects enrolled in a large molecular epidemiological cohort study validates that metabolic pathway involvement is a statistically significant factor for the conditional dependence between metabolites. Furthermore, new relationships among metabolites are discovered which can not be identified by the conventional methods of pathway analysis. Some of them have been widely recognized in biological literature.
This paper proposes an approach to implement TSK model by using a self-constructing fuzzy neural network (SCFNN). This network is built based on ellipsoidal basis function (EBF), which can be divided into two parts. T...
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ISBN:
(纸本)9780769535715
This paper proposes an approach to implement TSK model by using a self-constructing fuzzy neural network (SCFNN). This network is built based on ellipsoidal basis function (EBF), which can be divided into two parts. The first hidden layer composed of EBF units is considered as IF-part, and the output layer which consists of the connect weights is the THEN-part. The structure of SCFNN can adjust adaptively by a new structure learning algorithm based on the proposed crucial factor which denotes the importance of a fuzzy rule. Thus, a rule can be generated or pruned automatically according to both the firing strength of the rule and the performance of SCFNN. Simulation results show that the SCFNN has the powerful capability to extract fuzzy rules in the network. Comprehensive comparisons with other approaches indicate that the proposed method is better considering the learning efficiency and actual effect.
The availability and the autonomy of local power systems supplied from renewable sources are the main subject of the paper. Due to pure random nature of solar and wind characteristics, the Bayes network methodology wa...
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ISBN:
(纸本)9781538650622
The availability and the autonomy of local power systems supplied from renewable sources are the main subject of the paper. Due to pure random nature of solar and wind characteristics, the Bayes network methodology was selected to study the available generated power of given resources non-optimally located but near the load. The Bayes networks were generated from a large database. The corresponding information was recorded using a professional meteorological station while the Essential Graph Search was the algorithm to generate the final Bayes network structure and parameters. The network allows for weather estimation also. The final results were validated by meteorological experts.
We consider the structurelearning problem for graphical models that we call loosely connected Markov random fields, in which the number of short paths between any pair of nodes is small, and present a new conditional...
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We consider the structurelearning problem for graphical models that we call loosely connected Markov random fields, in which the number of short paths between any pair of nodes is small, and present a new conditional independence test based algorithm for learning the underlying graph structure. The novel maximization step in our algorithm ensures that the true edges are detected correctly even when there are short cycles in the graph. The number of samples required by our algorithm is C log p , where p is the size of the graph and the constant C depends on the parameters of the model. We show that several previously studied models are examples of loosely connected Markov random fields, and our algorithm achieves the same or lower computational complexity than the previously designed algorithms for individual cases. We also get new results for more general graphical models, in particular, our algorithm learns general Ising models on the Erdős-Rényi random graph 𝒢 ( p , c p ) correctly with running time O ( np 5 ).
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