TDSDMI: Inference of time-delayed gene regulatory network using S-system model with delayed mutual information

Yang, Bin; Zhang, Wei; Wang, Haifeng; Song, Chuandong; Chen, Yuehui

doi:10.1016/j.compbiomed.2016.03.024

Cited by 9 publications

(5 citation statements)

References 29 publications

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“…The first artificial dataset is from a 30-gene time-delayed GRN, which is shown in Fig. 8 19,20 . Kimura’s method (S-system model based on decomposition strategy and a cooperative coevolutionary algorithm) 21 , DBN (dynamic Bayesian network learned by the likelihood maximization) 22 and TDSS (time-delayed S-system model based on PSO) 23 are also applied for 30-gene artificial TDGRN identification.…”

Section: Methodsmentioning

confidence: 99%

“…But in Chowdhury’s method, differential evolution (DE) algorithm was utilized to optimize all parameters in a TDSS model, and the computing load is very large for the large-scale GRN. In order to reduce computing load, we proposed restricted gene expression programming (RGEP) and particle swarm optimization (PSO) to evolve the TDSS model 20 . This method could select TFs automatically and the number of optimized parameters is reduced greatly.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Inference of Large-scale Time-delayed Gene Regulatory Network with Parallel MapReduce Cloud Platform

Yang

Bao

Huang

et al. 2018

Sci Rep

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Inference of gene regulatory network (GRN) is crucial to understand intracellular physiological activity and function of biology. The identification of large-scale GRN has been a difficult and hot topic of system biology in recent years. In order to reduce the computation load for large-scale GRN identification, a parallel algorithm based on restricted gene expression programming (RGEP), namely MPRGEP, is proposed to infer instantaneous and time-delayed regulatory relationships between transcription factors and target genes. In MPRGEP, the structure and parameters of time-delayed S-system (TDSS) model are encoded into one chromosome. An original hybrid optimization approach based on genetic algorithm (GA) and gene expression programming (GEP) is proposed to optimize TDSS model with MapReduce framework. Time-delayed GRNs (TDGRN) with hundreds of genes are utilized to test the performance of MPRGEP. The experiment results reveal that MPRGEP could infer more accurately gene regulatory network than other state-of-art methods, and obtain the convincing speedup.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Inference of Large-scale Time-delayed Gene Regulatory Network with Parallel MapReduce Cloud Platform

Yang

Bao

Huang

et al. 2018

Sci Rep

Self Cite

View full text Add to dashboard Cite

show abstract

“…To be favorable to construct the gene regulation network, the time delay is normally regarded as the integral multiple of time interval. Based on this, Yang et al [16] firstly established the time-delay gene expression matrix to dig the time-delay regulation relationship among the genes through the decision tree classifier. Yang [17] constructed the multi-timedelay gene regulation network by using the high-order Markov dynamic Bayesian network.…”

Section: Related Workmentioning

confidence: 99%

Research on Multi-Time-Delay Gene Regulation Network Based on Fuzzy Label Propagation

Zhang

2020

Journal of Healthcare Engineering

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In view of time delay existing in gene regulation, by using the analysis idea and methods of complex network, this paper proposes a multi-time-delay gene regulation network analysis method based on the fuzzy label propagation. The algorithm takes the relative change trend coefficient, the correlation coefficient, and the mutual information as the similarity measurement indexes for the gene pair, fully reflecting the correlation of gene pairs and simultaneously obtaining the gene regulation relationship and the time delay through the fuzzy label propagation algorithm of the semisupervised learning. Experimental results of the cell cycle-regulated genes of yeast show that the proposed construction method of GRN can not only correctly select potential regulation genes but also provide details about the gene regulator model, thereby more accurately constructing gene regulation network.

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“…GRN, however is a complex system and has some characteristics, such as strong coupling, random, time-delayed, strongly nonlinear, etc. To accurately capture the properties of GRN, nonlinear ODE model were proposed, such as S-system model [14]- [17]. Mazur et al reconstructed nonlinear differential equation model of gene regulation using stochastic sampling and Hill-type functions were added into the formal of ODE [18].…”

Section: Bayesian Network (Bn) Model Could Not Consider Dynamicmentioning

confidence: 99%

RNDEtree: Regulatory Network With Differential Equation Based on Flexible Neural Tree With Novel Criterion Function

Yang

Bao

2019

IEEE Access

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Gene regulatory network (GRN) could provide guidance for understanding the internal laws of biological phenomena and analyzing several diseases. Ordinary differential equation model, which owns continuity and flexibility, has been utilized to identify GRN over the past decade. In this paper, we propose a novel algorithm, which is named as RNDEtree, a nonlinear ordinary differential equation model based on a flexible neural tree to improve the accuracy of the GRN reconstruction. In this model, a flexible neural tree can be utilized to approximate the nonlinear regulation function of an ordinary differential equation model. Multiexpression programming is proposed to evolve the structure of a flexible neural tree, and the brainstorm optimization algorithm is utilized to optimize the parameters of the RNDEtree model. In order to improve the false-positive ratio of this method, a novel fitness function is proposed, in which sparse and minimum redundancy maximum relevance (mRMR) terms are considered when optimizing RNDEtree. The performances of our proposed algorithm can be evaluated by the benchmark datasets from the DREAM challenge and real biological dataset in E. coli. The experimental results demonstrate that the proposed method could infer more correctly GRN than the other state-the-art methods.INDEX TERMS Gene regulatory network, flexible neural tree model, ordinary differential equation, mutual information, minimum redundancy maximum relevance.

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TDSDMI: Inference of time-delayed gene regulatory network using S-system model with delayed mutual information

Cited by 9 publications

References 29 publications

Inference of Large-scale Time-delayed Gene Regulatory Network with Parallel MapReduce Cloud Platform

Inference of Large-scale Time-delayed Gene Regulatory Network with Parallel MapReduce Cloud Platform

Research on Multi-Time-Delay Gene Regulation Network Based on Fuzzy Label Propagation

RNDEtree: Regulatory Network With Differential Equation Based on Flexible Neural Tree With Novel Criterion Function

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