Inferring Gene Regulatory Networks Using Conditional Regulation Pattern to Guide Candidate Genes

Xiao, Fei; Gao, Lin; Ye, Yusen; Hu, Yuxuan; He, Ruijie

doi:10.1371/journal.pone.0154953

Cited by 17 publications

(10 citation statements)

References 31 publications

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“…Also, a null model with closed-form solution is possible according to previous work [ 44 ] in which the author hypothesized connectivity matrix for inter dataset regulatory networks. Beyond current ARACNE framework, there are many other ways to achieve further parallelism, such as data level parallelism.…”

Section: Discussionmentioning

confidence: 99%

Accelerated parallel algorithm for gene network reverse engineering

Zhou²,

Reed³

et al. 2017

BMC Syst Biol

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BackgroundThe Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNE) represents one of the most effective tools to reconstruct gene regulatory networks from large-scale molecular profile datasets. However, previous implementations require intensive computing resources and, in some cases, restrict the number of samples that can be used. These issues can be addressed elegantly in a GPU computing framework, where repeated mathematical computation can be done efficiently, but requires extensive redesign to apply parallel computing techniques to the original serial algorithm, involving detailed optimization efforts based on a deep understanding of both hardware and software architecture.ResultHere, we present an accelerated parallel implementation of ARACNE (GPU-ARACNE). By taking advantage of multi-level parallelism and the Compute Unified Device Architecture (CUDA) parallel kernel-call library, GPU-ARACNE successfully parallelizes a serial algorithm and simplifies the user experience from multi-step operations to one step. Using public datasets on comparable hardware configurations, we showed that GPU-ARACNE is faster than previous implementations and is able to reconstruct equally valid gene regulatory networks.ConclusionGiven that previous versions of ARACNE are extremely resource demanding, either in computational time or in hardware investment, GPU-ARACNE is remarkably valuable for researchers who need to build complex regulatory networks from large expression datasets, but with limited budget on computational resources. In addition, our GPU-centered optimization of adaptive partitioning for Mutual Information (MI) estimation provides lessons that are applicable to other domains.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-017-0458-5) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

Accelerated parallel algorithm for gene network reverse engineering

Zhou²,

Reed³

et al. 2017

BMC Syst Biol

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“…network dynamics inference). In fact, most previous methods based on Bayesian networks ( de Matos Simoes and Emmert-Streib, 2012 ; Liu et al , 2016 ), graphical Gaussian models ( Krämer et al , 2009 ; Menéndez et al , 2010 ), information theory approaches ( Margolin et al , 2006 ; Xiao et al , 2016 ), correlation approaches ( Yu et al , 2017 ; Zuo et al , 2014 ) and supervised learning approaches ( Huynh-Thu et al , 2010 ; Kotera et al , 2012 ) have focused on only the network structure inference. On the other hand, a few methods using Boolean models ( Kauffman, 1969 ; 1993 ), or differential equation-based models ( Coddington and Levinson, 1955 ) have been proposed to predict both the network structure and dynamics.…”

Section: Introductionmentioning

confidence: 99%

A novel constrained genetic algorithm-based Boolean network inference method from steady-state gene expression data

Trinh

Kwon

2021

Bioinformatics

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Motivation It is a challenging problem in systems biology to infer both the network structure and dynamics of a gene regulatory network from steady-state gene expression data. Some methods based on Boolean or differential equation models have been proposed but they were not efficient in inference of large-scale networks. Therefore, it is necessary to develop a method to infer the network structure and dynamics accurately on large-scale networks using steady-state expression. Results In this study, we propose a novel constrained genetic algorithm-based Boolean network inference (CGA-BNI) method where a Boolean canalyzing update rule scheme was employed to capture coarse-grained dynamics. Given steady-state gene expression data as an input, CGA-BNI identifies a set of path consistency-based constraints by comparing the gene expression level between the wild-type and the mutant experiments. It then searches Boolean networks which satisfy the constraints and induce attractors most similar to steady-state expressions. We devised a heuristic mutation operation for faster convergence and implemented a parallel evaluation routine for execution time reduction. Through extensive simulations on the artificial and the real gene expression datasets, CGA-BNI showed better performance than four other existing methods in terms of both structural and dynamics prediction accuracies. Taken together, CGA-BNI is a promising tool to predict both the structure and the dynamics of a gene regulatory network when a highest accuracy is needed at the cost of sacrificing the execution time. Availability and implementation Source code and data are freely available at https://github.com/csclab/CGA-BNI. Supplementary information Supplementary data are available at Bioinformatics online.

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“…Bayesian networks have been used to model gene expression data 8 – 15 and gene regulatory networks 16 – 20 . A BN consists of a directed acyclic graph (DAG) 21 , 22 and a set of corresponding conditional probability density functions.…”

Section: Introductionmentioning

confidence: 99%

Applications of Bayesian network models in predicting types of hematological malignancies

Agrahari

Foroushani

Docking

et al. 2018

Sci Rep

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Network analysis is the preferred approach for the detection of subtle but coordinated changes in expression of an interacting and related set of genes. We introduce a novel method based on the analyses of coexpression networks and Bayesian networks, and we use this new method to classify two types of hematological malignancies; namely, acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS). Our classifier has an accuracy of 93%, a precision of 98%, and a recall of 90% on the training dataset (n = 366); which outperforms the results reported by other scholars on the same dataset. Although our training dataset consists of microarray data, our model has a remarkable performance on the RNA-Seq test dataset (n = 74, accuracy = 89%, precision = 88%, recall = 98%), which confirms that eigengenes are robust with respect to expression profiling technology. These signatures are useful in classification and correctly predicting the diagnosis. They might also provide valuable information about the underlying biology of diseases. Our network analysis approach is generalizable and can be useful for classifying other diseases based on gene expression profiles. Our previously published Pigengene package is publicly available through Bioconductor, which can be used to conveniently fit a Bayesian network to gene expression data.

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Inferring Gene Regulatory Networks Using Conditional Regulation Pattern to Guide Candidate Genes

Cited by 17 publications

References 31 publications

Accelerated parallel algorithm for gene network reverse engineering

Accelerated parallel algorithm for gene network reverse engineering

A novel constrained genetic algorithm-based Boolean network inference method from steady-state gene expression data

Applications of Bayesian network models in predicting types of hematological malignancies

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