Gene Expression Network Reconstruction by LEP Method Using Microarray Data

Gene expression network reconstruction using microarray data is widely studied aiming to investigate the behavior of a gene cluster simultaneously. Under the Gaussian assumption, the conditional dependence between genes in the network is fully described by the partial correlation coefficient matrix....

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Detalles Bibliográficos
Autores principales: You, Na, Mou, Peng, Qiu, Ting, Kou, Qiang, Zhu, Huaijin, Chen, Yuexi, Wang, Xueqin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Scientific World Journal 2012
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3540759/
https://www.ncbi.nlm.nih.gov/pubmed/23365528
http://dx.doi.org/10.1100/2012/753430
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author You, Na
Mou, Peng
Qiu, Ting
Kou, Qiang
Zhu, Huaijin
Chen, Yuexi
Wang, Xueqin
author_facet You, Na
Mou, Peng
Qiu, Ting
Kou, Qiang
Zhu, Huaijin
Chen, Yuexi
Wang, Xueqin
author_sort You, Na
collection PubMed
description Gene expression network reconstruction using microarray data is widely studied aiming to investigate the behavior of a gene cluster simultaneously. Under the Gaussian assumption, the conditional dependence between genes in the network is fully described by the partial correlation coefficient matrix. Due to the high dimensionality and sparsity, we utilize the LEP method to estimate it in this paper. Compared to the existing methods, the LEP reaches the highest PPV with the sensitivity controlled at the satisfactory level. A set of gene expression data from the HapMap project is analyzed for illustration.
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spelling pubmed-35407592013-01-30 Gene Expression Network Reconstruction by LEP Method Using Microarray Data You, Na Mou, Peng Qiu, Ting Kou, Qiang Zhu, Huaijin Chen, Yuexi Wang, Xueqin ScientificWorldJournal Research Article Gene expression network reconstruction using microarray data is widely studied aiming to investigate the behavior of a gene cluster simultaneously. Under the Gaussian assumption, the conditional dependence between genes in the network is fully described by the partial correlation coefficient matrix. Due to the high dimensionality and sparsity, we utilize the LEP method to estimate it in this paper. Compared to the existing methods, the LEP reaches the highest PPV with the sensitivity controlled at the satisfactory level. A set of gene expression data from the HapMap project is analyzed for illustration. The Scientific World Journal 2012-12-23 /pmc/articles/PMC3540759/ /pubmed/23365528 http://dx.doi.org/10.1100/2012/753430 Text en Copyright © 2012 Na You et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
You, Na
Mou, Peng
Qiu, Ting
Kou, Qiang
Zhu, Huaijin
Chen, Yuexi
Wang, Xueqin
Gene Expression Network Reconstruction by LEP Method Using Microarray Data
title Gene Expression Network Reconstruction by LEP Method Using Microarray Data
title_full Gene Expression Network Reconstruction by LEP Method Using Microarray Data
title_fullStr Gene Expression Network Reconstruction by LEP Method Using Microarray Data
title_full_unstemmed Gene Expression Network Reconstruction by LEP Method Using Microarray Data
title_short Gene Expression Network Reconstruction by LEP Method Using Microarray Data
title_sort gene expression network reconstruction by lep method using microarray data
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3540759/
https://www.ncbi.nlm.nih.gov/pubmed/23365528
http://dx.doi.org/10.1100/2012/753430
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