Improved Bayesian Network inference using relaxed gene ordering

Zhu, Dongxiao; Li, Hua

doi:10.1504/ijdmb.2010.030966

Cited by 6 publications

(2 citation statements)

References 40 publications

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“…On the other hand, model-based methods can be further classified into ordinary differential equation [ 14 , 15 ], multiple linear regression [ 18 , 19 ], linear programming [ 20 , 21 ], Boolean networks [ 17 , 22 ], and probabilistic graphical models including Bayesian network (BN) [ 3 , 16 , 23 , 49 ] and graphical Gaussian model [ 24 , 25 ]. Overall, these model-based methods can provide us a deeper understanding of the system’s behaviors at a network level and can also infer the directions of regulations in the network.…”

Section: Introductionmentioning

confidence: 99%

Inference of Gene Regulatory Network Based on Local Bayesian Networks

et al. 2016

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The inference of gene regulatory networks (GRNs) from expression data can mine the direct regulations among genes and gain deep insights into biological processes at a network level. During past decades, numerous computational approaches have been introduced for inferring the GRNs. However, many of them still suffer from various problems, e.g., Bayesian network (BN) methods cannot handle large-scale networks due to their high computational complexity, while information theory-based methods cannot identify the directions of regulatory interactions and also suffer from false positive/negative problems. To overcome the limitations, in this work we present a novel algorithm, namely local Bayesian network (LBN), to infer GRNs from gene expression data by using the network decomposition strategy and false-positive edge elimination scheme. Specifically, LBN algorithm first uses conditional mutual information (CMI) to construct an initial network or GRN, which is decomposed into a number of local networks or GRNs. Then, BN method is employed to generate a series of local BNs by selecting the k-nearest neighbors of each gene as its candidate regulatory genes, which significantly reduces the exponential search space from all possible GRN structures. Integrating these local BNs forms a tentative network or GRN by performing CMI, which reduces redundant regulations in the GRN and thus alleviates the false positive problem. The final network or GRN can be obtained by iteratively performing CMI and local BN on the tentative network. In the iterative process, the false or redundant regulations are gradually removed. When tested on the benchmark GRN datasets from DREAM challenge as well as the SOS DNA repair network in E.coli, our results suggest that LBN outperforms other state-of-the-art methods (ARACNE, GENIE3 and NARROMI) significantly, with more accurate and robust performance. In particular, the decomposition strategy with local Bayesian networks not only effectively reduce the computational cost of BN due to much smaller sizes of local GRNs, but also identify the directions of the regulations.

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Section: Introductionmentioning

confidence: 99%

Inference of Gene Regulatory Network Based on Local Bayesian Networks

et al. 2016

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“…Compared with pairwise similarity based network discovery methods, Bayesian network approaches are more powerful since they consider many-to-one gene dependencies [ 2 , 3 , 21 ]. Numerous strategies for network scoring and searching have been proposed, such as Bayesian Dirichlet (BD) [ 22 ], K2 [ 23 ] and MCMC [ 24 ].…”

Section: Introductionmentioning

confidence: 99%

Network inference through synergistic subnetwork evolution

Acharya¹,

Reynolds

Zhu

2015

J Bioinform Sys Biology

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Study of signaling networks is important for a better understanding of cell behaviors e.g., growth, differentiation, metabolism, proptosis, and gaining deeper insights into the molecular mechanisms of complex diseases. While there have been many successes in developing computational approaches for identifying potential genes and proteins involved in cell signaling, new methods are needed for identifying network structures that depict underlying signal cascading mechanisms. In this paper, we propose a new computational approach for inferring signaling network structures from overlapping gene sets related to the networks. In the proposed approach, a signaling network is represented as a directed graph and is viewed as a union of many active paths representing linear and overlapping chains of signal cascading activities in the network. Gene sets represent the sets of genes participating in active paths without prior knowledge of the order in which genes occur within each path. From a compendium of unordered gene sets, the proposed algorithm reconstructs the underlying network structure through evolution of synergistic active paths. In our context, the extent of edge overlapping among active paths is used to define the synergy present in a network. We evaluated the performance of the proposed algorithm in terms of its convergence and recovering true active paths by utilizing four gene set compendiums derived from the KEGG database. Evaluation of results demonstrate the ability of the algorithm in reconstructing the underlying networks with high accuracy and precision.

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An order independent algorithm for inferring gene regulatory network using quantile value for conditional independence tests

Mahmoodi

Aghdam

Eslahchi

2021

Sci Rep

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In recent years, due to the difficulty and inefficiency of experimental methods, numerous computational methods have been introduced for inferring the structure of Gene Regulatory Networks (GRNs). The Path Consistency (PC) algorithm is one of the popular methods to infer the structure of GRNs. However, this group of methods still has limitations and there is a potential for improvements in this field. For example, the PC-based algorithms are still sensitive to the ordering of nodes i.e. different node orders results in different network structures. The second is that the networks inferred by these methods are highly dependent on the threshold used for independence testing. Also, it is still a challenge to select the set of conditional genes in an optimal way, which affects the performance and computation complexity of the PC-based algorithm. We introduce a novel algorithm, namely Order Independent PC-based algorithm using Quantile value (OIPCQ), which improves the accuracy of the learning process of GRNs and solves the order dependency issue. The quantile-based thresholds are considered for different orders of CMI tests. For conditional gene selection, we consider the paths between genes with length equal or greater than 2 while other well-known PC-based methods only consider the paths of length 2. We applied OIPCQ on the various networks of the DREAM3 and DREAM4 in silico challenges. As a real-world case study, we used OIPCQ to reconstruct SOS DNA network obtained from Escherichia coli and GRN for acute myeloid leukemia based on the RNA sequencing data from The Cancer Genome Atlas. The results show that OIPCQ produces the same network structure for all the permutations of the genes and improves the resulted GRN through accurately quantifying the causal regulation strength in comparison with other well-known PC-based methods. According to the GRN constructed by OIPCQ, for acute myeloid leukemia, two regulators BCLAF1 and NRSF reported previously are significantly important. However, the highest degree nodes in this GRN are ZBTB7A and PU1 which play a significant role in cancer, especially in leukemia. OIPCQ is freely accessible at https://github.com/haammim/OIPCQ-and-OIPCQ2.

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Improved Bayesian Network inference using relaxed gene ordering

Cited by 6 publications

References 40 publications

Inference of Gene Regulatory Network Based on Local Bayesian Networks

Inference of Gene Regulatory Network Based on Local Bayesian Networks

Network inference through synergistic subnetwork evolution

An order independent algorithm for inferring gene regulatory network using quantile value for conditional independence tests

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