Gene Expression Network Reconstruction by LEP Method Using Microarray Data

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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Main Authors: Na You, Peng Mou, Ting Qiu, Qiang Kou, Huaijin Zhu, Yuexi Chen, Xueqin Wang
Format: Article
Language:English
Published: Hindawi Limited 2012-01-01
Series:The Scientific World Journal
Online Access:http://dx.doi.org/10.1100/2012/753430
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spelling doaj-4556da9fb4a54bef9aa3f87289c57de72020-11-25T02:46:34ZengHindawi LimitedThe Scientific World Journal1537-744X2012-01-01201210.1100/2012/753430753430Gene Expression Network Reconstruction by LEP Method Using Microarray DataNa You0Peng Mou1Ting Qiu2Qiang Kou3Huaijin Zhu4Yuexi Chen5Xueqin Wang6School of Mathematics & Computational Science, Sun Yat-Sen University, Guangzhou, Guangdong 510275, ChinaSchool of Mathematics & Computational Science, Sun Yat-Sen University, Guangzhou, Guangdong 510275, ChinaSchool of Mathematics & Computational Science, Sun Yat-Sen University, Guangzhou, Guangdong 510275, ChinaSchool of Mathematics & Computational Science, Sun Yat-Sen University, Guangzhou, Guangdong 510275, ChinaSchool of Mathematics & Computational Science, Sun Yat-Sen University, Guangzhou, Guangdong 510275, ChinaSchool of Mathematics & Computational Science, Sun Yat-Sen University, Guangzhou, Guangdong 510275, ChinaSchool of Mathematics & Computational Science, Sun Yat-Sen University, Guangzhou, Guangdong 510275, ChinaGene 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.http://dx.doi.org/10.1100/2012/753430
collection DOAJ
language English
format Article
sources DOAJ
author Na You
Peng Mou
Ting Qiu
Qiang Kou
Huaijin Zhu
Yuexi Chen
Xueqin Wang
spellingShingle Na You
Peng Mou
Ting Qiu
Qiang Kou
Huaijin Zhu
Yuexi Chen
Xueqin Wang
Gene Expression Network Reconstruction by LEP Method Using Microarray Data
The Scientific World Journal
author_facet Na You
Peng Mou
Ting Qiu
Qiang Kou
Huaijin Zhu
Yuexi Chen
Xueqin Wang
author_sort Na You
title Gene Expression Network Reconstruction by LEP Method Using Microarray Data
title_short 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_sort gene expression network reconstruction by lep method using microarray data
publisher Hindawi Limited
series The Scientific World Journal
issn 1537-744X
publishDate 2012-01-01
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.
url http://dx.doi.org/10.1100/2012/753430
work_keys_str_mv AT nayou geneexpressionnetworkreconstructionbylepmethodusingmicroarraydata
AT pengmou geneexpressionnetworkreconstructionbylepmethodusingmicroarraydata
AT tingqiu geneexpressionnetworkreconstructionbylepmethodusingmicroarraydata
AT qiangkou geneexpressionnetworkreconstructionbylepmethodusingmicroarraydata
AT huaijinzhu geneexpressionnetworkreconstructionbylepmethodusingmicroarraydata
AT yuexichen geneexpressionnetworkreconstructionbylepmethodusingmicroarraydata
AT xueqinwang geneexpressionnetworkreconstructionbylepmethodusingmicroarraydata
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