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A Sparse Reconstruction Approach for Identifying Gene Regulatory Networks Using Steady-State Experiment Data.

Zhang W, Zhou T - PLoS ONE (2015)

Bottom Line: Efficiency of this method is tested by an artificial linear network, a mitogen-activated protein kinase (MAPK) pathway network and the in silico networks of the DREAM challenges.The performance of the suggested approach is compared with two state-of-the-art algorithms, the widely adopted total least-squares (TLS) method and those available results on the DREAM project.Actual results show that, with a lower computational cost, the proposed method can significantly enhance estimation accuracy and greatly reduce false positive and negative errors.

View Article: PubMed Central - PubMed

Affiliation: School of Chemical Machinery, Qinghai University, Qinghai, China; Department of Automation, Tsinghua University, Beijing, China.

ABSTRACT

Motivation: Identifying gene regulatory networks (GRNs) which consist of a large number of interacting units has become a problem of paramount importance in systems biology. Situations exist extensively in which causal interacting relationships among these units are required to be reconstructed from measured expression data and other a priori information. Though numerous classical methods have been developed to unravel the interactions of GRNs, these methods either have higher computing complexities or have lower estimation accuracies. Note that great similarities exist between identification of genes that directly regulate a specific gene and a sparse vector reconstruction, which often relates to the determination of the number, location and magnitude of nonzero entries of an unknown vector by solving an underdetermined system of linear equations y = Φx. Based on these similarities, we propose a novel framework of sparse reconstruction to identify the structure of a GRN, so as to increase accuracy of causal regulation estimations, as well as to reduce their computational complexity.

Results: In this paper, a sparse reconstruction framework is proposed on basis of steady-state experiment data to identify GRN structure. Different from traditional methods, this approach is adopted which is well suitable for a large-scale underdetermined problem in inferring a sparse vector. We investigate how to combine the noisy steady-state experiment data and a sparse reconstruction algorithm to identify causal relationships. Efficiency of this method is tested by an artificial linear network, a mitogen-activated protein kinase (MAPK) pathway network and the in silico networks of the DREAM challenges. The performance of the suggested approach is compared with two state-of-the-art algorithms, the widely adopted total least-squares (TLS) method and those available results on the DREAM project. Actual results show that, with a lower computational cost, the proposed method can significantly enhance estimation accuracy and greatly reduce false positive and negative errors. Furthermore, numerical calculations demonstrate that the proposed algorithm may have faster convergence speed and smaller fluctuation than other methods when either estimate error or estimate bias is considered.

No MeSH data available.


Related in: MedlinePlus

Comparison of the averaged ROC and PR curves in the MAPK network identification using the SubLM1, SubLM2, TLS, SmOMP and StOMP algorithms.(a) Averaged ROC curves. (b) Averaged PR curves.
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pone.0130979.g003: Comparison of the averaged ROC and PR curves in the MAPK network identification using the SubLM1, SubLM2, TLS, SmOMP and StOMP algorithms.(a) Averaged ROC curves. (b) Averaged PR curves.

Mentions: We consider five algorithms for comparison in a nonlinear MAPK network, which are SubLM1, SubLM2, TLS, SmOMP and StOMP. The averaged ROC and PR curves are shown in Fig 3. Additionally, the performance metrics of AUROC and AUPR and the averaged runtime are shown in Table 3. From these results, it is obvious that the SmOMP algorithm outperforms other methods.


A Sparse Reconstruction Approach for Identifying Gene Regulatory Networks Using Steady-State Experiment Data.

Zhang W, Zhou T - PLoS ONE (2015)

Comparison of the averaged ROC and PR curves in the MAPK network identification using the SubLM1, SubLM2, TLS, SmOMP and StOMP algorithms.(a) Averaged ROC curves. (b) Averaged PR curves.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4514654&req=5

pone.0130979.g003: Comparison of the averaged ROC and PR curves in the MAPK network identification using the SubLM1, SubLM2, TLS, SmOMP and StOMP algorithms.(a) Averaged ROC curves. (b) Averaged PR curves.
Mentions: We consider five algorithms for comparison in a nonlinear MAPK network, which are SubLM1, SubLM2, TLS, SmOMP and StOMP. The averaged ROC and PR curves are shown in Fig 3. Additionally, the performance metrics of AUROC and AUPR and the averaged runtime are shown in Table 3. From these results, it is obvious that the SmOMP algorithm outperforms other methods.

Bottom Line: Efficiency of this method is tested by an artificial linear network, a mitogen-activated protein kinase (MAPK) pathway network and the in silico networks of the DREAM challenges.The performance of the suggested approach is compared with two state-of-the-art algorithms, the widely adopted total least-squares (TLS) method and those available results on the DREAM project.Actual results show that, with a lower computational cost, the proposed method can significantly enhance estimation accuracy and greatly reduce false positive and negative errors.

View Article: PubMed Central - PubMed

Affiliation: School of Chemical Machinery, Qinghai University, Qinghai, China; Department of Automation, Tsinghua University, Beijing, China.

ABSTRACT

Motivation: Identifying gene regulatory networks (GRNs) which consist of a large number of interacting units has become a problem of paramount importance in systems biology. Situations exist extensively in which causal interacting relationships among these units are required to be reconstructed from measured expression data and other a priori information. Though numerous classical methods have been developed to unravel the interactions of GRNs, these methods either have higher computing complexities or have lower estimation accuracies. Note that great similarities exist between identification of genes that directly regulate a specific gene and a sparse vector reconstruction, which often relates to the determination of the number, location and magnitude of nonzero entries of an unknown vector by solving an underdetermined system of linear equations y = Φx. Based on these similarities, we propose a novel framework of sparse reconstruction to identify the structure of a GRN, so as to increase accuracy of causal regulation estimations, as well as to reduce their computational complexity.

Results: In this paper, a sparse reconstruction framework is proposed on basis of steady-state experiment data to identify GRN structure. Different from traditional methods, this approach is adopted which is well suitable for a large-scale underdetermined problem in inferring a sparse vector. We investigate how to combine the noisy steady-state experiment data and a sparse reconstruction algorithm to identify causal relationships. Efficiency of this method is tested by an artificial linear network, a mitogen-activated protein kinase (MAPK) pathway network and the in silico networks of the DREAM challenges. The performance of the suggested approach is compared with two state-of-the-art algorithms, the widely adopted total least-squares (TLS) method and those available results on the DREAM project. Actual results show that, with a lower computational cost, the proposed method can significantly enhance estimation accuracy and greatly reduce false positive and negative errors. Furthermore, numerical calculations demonstrate that the proposed algorithm may have faster convergence speed and smaller fluctuation than other methods when either estimate error or estimate bias is considered.

No MeSH data available.


Related in: MedlinePlus