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A Sparse Reconstruction Approach for Identifying Gene Regulatory Networks Using Steady-State Experiment Data
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...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4514654/ https://www.ncbi.nlm.nih.gov/pubmed/26207991 http://dx.doi.org/10.1371/journal.pone.0130979 |
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author | Zhang, Wanhong Zhou, Tong |
author_facet | Zhang, Wanhong Zhou, Tong |
author_sort | Zhang, Wanhong |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-4514654 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-45146542015-07-29 A Sparse Reconstruction Approach for Identifying Gene Regulatory Networks Using Steady-State Experiment Data Zhang, Wanhong Zhou, Tong PLoS One Research Article 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. Public Library of Science 2015-07-24 /pmc/articles/PMC4514654/ /pubmed/26207991 http://dx.doi.org/10.1371/journal.pone.0130979 Text en © 2015 Zhang, Zhou http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Zhang, Wanhong Zhou, Tong A Sparse Reconstruction Approach for Identifying Gene Regulatory Networks Using Steady-State Experiment Data |
title | A Sparse Reconstruction Approach for Identifying Gene Regulatory Networks Using Steady-State Experiment Data |
title_full | A Sparse Reconstruction Approach for Identifying Gene Regulatory Networks Using Steady-State Experiment Data |
title_fullStr | A Sparse Reconstruction Approach for Identifying Gene Regulatory Networks Using Steady-State Experiment Data |
title_full_unstemmed | A Sparse Reconstruction Approach for Identifying Gene Regulatory Networks Using Steady-State Experiment Data |
title_short | A Sparse Reconstruction Approach for Identifying Gene Regulatory Networks Using Steady-State Experiment Data |
title_sort | sparse reconstruction approach for identifying gene regulatory networks using steady-state experiment data |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4514654/ https://www.ncbi.nlm.nih.gov/pubmed/26207991 http://dx.doi.org/10.1371/journal.pone.0130979 |
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