Inferring gene regulatory networks by singular value decomposition and gravitation field algorithm.
Inferring gene regulatory networks by singular value decomposition and gravitation field algorithm.
复制标题
通过奇异值分解和引力场算法推断基因调控网络
DOI：
10.1371/journal.pone.0051141
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发表时间：
2012
期刊：
影响因子：
3.7
通讯作者：
Zhou CG
中科院分区：
文献类型：
--
作者：
Zheng M;Wu JN;Huang YX;Liu GX;Zhou Y;Zhou CG
Reconstruction of gene regulatory networks (GRNs) is of utmost interest and has become a challenge computational problem in system biology. However, every existing inference algorithm from gene expression profiles has its own advantages and disadvantages. In particular, the effectiveness and efficiency of every previous algorithm is not high enough. In this work, we proposed a novel inference algorithm from gene expression data based on differential equation model. In this algorithm, two methods were included for inferring GRNs. Before reconstructing GRNs, singular value decomposition method was used to decompose gene expression data, determine the algorithm solution space, and get all candidate solutions of GRNs. In these generated family of candidate solutions, gravitation field algorithm was modified to infer GRNs, used to optimize the criteria of differential equation model, and search the best network structure result. The proposed algorithm is validated on both the simulated scale-free network and real benchmark gene regulatory network in networks database. Both the Bayesian method and the traditional differential equation model were also used to infer GRNs, and the results were used to compare with the proposed algorithm in our work. And genetic algorithm and simulated annealing were also used to evaluate gravitation field algorithm. The cross-validation results confirmed the effectiveness of our algorithm, which outperforms significantly other previous algorithms.
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通讯作者：
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Giacobini M
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3.7
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通讯作者：
de Givry S