PPARgene: A Database of Experimentally Verified and Computationally Predicted PPAR Target Genes
The peroxisome proliferator-activated receptors (PPARs) are ligand-activated transcription factors of the nuclear receptor superfamily. Upon ligand binding, PPARs activate target gene transcription and regulate a variety of important physiological processes such as lipid metabolism, inflammation, an...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2016-01-01
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| Series: | PPAR Research |
| Online Access: | http://dx.doi.org/10.1155/2016/6042162 |
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| _version_ | 1850237096960196608 |
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| author | Li Fang Man Zhang Yanhui Li Yan Liu Qinghua Cui Nanping Wang |
| author_facet | Li Fang Man Zhang Yanhui Li Yan Liu Qinghua Cui Nanping Wang |
| author_sort | Li Fang |
| collection | DOAJ |
| description | The peroxisome proliferator-activated receptors (PPARs) are ligand-activated transcription factors of the nuclear receptor superfamily. Upon ligand binding, PPARs activate target gene transcription and regulate a variety of important physiological processes such as lipid metabolism, inflammation, and wound healing. Here, we describe the first database of PPAR target genes, PPARgene. Among the 225 experimentally verified PPAR target genes, 83 are for PPARα, 83 are for PPARβ/δ, and 104 are for PPARγ. Detailed information including tissue types, species, and reference PubMed IDs was also provided. In addition, we developed a machine learning method to predict novel PPAR target genes by integrating in silico PPAR-responsive element (PPRE) analysis with high throughput gene expression data. Fivefold cross validation showed that the performance of this prediction method was significantly improved compared to the in silico PPRE analysis method. The prediction tool is also implemented in the PPARgene database. |
| format | Article |
| id | doaj-art-1ba67bb1dcfc4efaa915cc06f325024b |
| institution | OA Journals |
| issn | 1687-4757 1687-4765 |
| language | English |
| publishDate | 2016-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | PPAR Research |
| spelling | doaj-art-1ba67bb1dcfc4efaa915cc06f325024b2025-08-20T02:01:50ZengWileyPPAR Research1687-47571687-47652016-01-01201610.1155/2016/60421626042162PPARgene: A Database of Experimentally Verified and Computationally Predicted PPAR Target GenesLi Fang0Man Zhang1Yanhui Li2Yan Liu3Qinghua Cui4Nanping Wang5Institute of Cardiovascular Sciences, Peking University Health Science Center, Beijing 100191, ChinaInstitute of Cardiovascular Sciences, Peking University Health Science Center, Beijing 100191, ChinaInstitute of Cardiovascular Sciences, Peking University Health Science Center, Beijing 100191, ChinaInstitute of Cardiovascular Sciences, Peking University Health Science Center, Beijing 100191, ChinaDepartment of Biomedical Informatics, Peking University Health Science Center, Beijing 100191, ChinaInstitute of Cardiovascular Sciences, Peking University Health Science Center, Beijing 100191, ChinaThe peroxisome proliferator-activated receptors (PPARs) are ligand-activated transcription factors of the nuclear receptor superfamily. Upon ligand binding, PPARs activate target gene transcription and regulate a variety of important physiological processes such as lipid metabolism, inflammation, and wound healing. Here, we describe the first database of PPAR target genes, PPARgene. Among the 225 experimentally verified PPAR target genes, 83 are for PPARα, 83 are for PPARβ/δ, and 104 are for PPARγ. Detailed information including tissue types, species, and reference PubMed IDs was also provided. In addition, we developed a machine learning method to predict novel PPAR target genes by integrating in silico PPAR-responsive element (PPRE) analysis with high throughput gene expression data. Fivefold cross validation showed that the performance of this prediction method was significantly improved compared to the in silico PPRE analysis method. The prediction tool is also implemented in the PPARgene database.http://dx.doi.org/10.1155/2016/6042162 |
| spellingShingle | Li Fang Man Zhang Yanhui Li Yan Liu Qinghua Cui Nanping Wang PPARgene: A Database of Experimentally Verified and Computationally Predicted PPAR Target Genes PPAR Research |
| title | PPARgene: A Database of Experimentally Verified and Computationally Predicted PPAR Target Genes |
| title_full | PPARgene: A Database of Experimentally Verified and Computationally Predicted PPAR Target Genes |
| title_fullStr | PPARgene: A Database of Experimentally Verified and Computationally Predicted PPAR Target Genes |
| title_full_unstemmed | PPARgene: A Database of Experimentally Verified and Computationally Predicted PPAR Target Genes |
| title_short | PPARgene: A Database of Experimentally Verified and Computationally Predicted PPAR Target Genes |
| title_sort | ppargene a database of experimentally verified and computationally predicted ppar target genes |
| url | http://dx.doi.org/10.1155/2016/6042162 |
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