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PPAR Research
Volume 2016, Article ID 6042162, 6 pages
http://dx.doi.org/10.1155/2016/6042162
Research Article

PPARgene: A Database of Experimentally Verified and Computationally Predicted PPAR Target Genes

1Institute of Cardiovascular Sciences, Peking University Health Science Center, Beijing 100191, China
2Department of Biomedical Informatics, Peking University Health Science Center, Beijing 100191, China
3The Advanced Institute for Medical Sciences, Dalian Medical University, Dalian 116044, China

Received 21 January 2016; Accepted 24 March 2016

Academic Editor: Todd Leff

Copyright © 2016 Li Fang et al. 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.

Abstract

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.