Research On The Construction Of Gene Regulatory Networks Based On The Microarray Data

Gene regulatory network is formed by the interaction of genes within cells.It is the main mechanism for controlling gene expression in organisms.Constructing gene regulatory network is one of the important means to understand the nature of life activities.Therefore,the use of high-throughput experimental data,especially microarray data to construct gene regulatory network has become a hot topic in the field of systems biology.However,most of the existing methods for constructing gene regulatory networks based on microarray are difficult to determine the direction of regulation or the complexity of computing.Therefore,this paper proposes a genetic regulatory network construction method based on the existing correlation test method and the ordinary differential equation modeling method.Firstly,the Pearson correlation coefficients between genes were calculated by using perturbation experimental data,and then an initial gene regulatory network was constructed by sorting the Z scores.On this basis,the initial control network is optimized by using time series data and ordinary differential equations modeling.After model building of the ordinary differential equation,the problem of gene network inference is transformed into a model parameter estimation problem.In this paper,a tabu search built-in particle swarm optimization algorithm is proposed,which is called tabu particle swarm optimization to estimate model parameters.In order to reduce the computational complexity,the curve fitting method is used to fit the spectral data of time series,estimate the differential of each time point.In this way,the parameter estimation problem of differential equations is transformed into a pseudo multiple linear regression problem,and the computation time is greatly reduced.Finally,we validate the proposed method by using standard test set and real microarray data.The results show that the sensitivity,specificity and accuracy of the proposed algorithm in the construction of gene regulatory networks have been improved compared with the existing methods.At the same time,the calculation speed of this paper is faster than the existing methods.