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BioMed Research International
Volume 2014, Article ID 484926, 10 pages
http://dx.doi.org/10.1155/2014/484926
Research Article

System Analysis of LWDH Related Genes Based on Text Mining in Biological Networks

1College of Life Sciences, Northwest A&F University, Yangling, Shaanxi 712100, China
2College of Pharmacy, Nankai University, Tianjin 300071, China
3College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
4Department of Statistics, Rice University, 6100 Main Street, Houston, TX 77005, USA
5Department of Nutrition and Food Hygiene, School of Public Health, Harbin Medical University, Harbin 150081, China
6College of Life Science, Anhui Agricultural University, Hefei 230036, China
7Genome Analysis Laboratory, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China

Received 15 May 2014; Revised 2 July 2014; Accepted 3 July 2014; Published 27 August 2014

Academic Editor: Tatsuya Akutsu

Copyright © 2014 Mingzhi Liao 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

Liuwei-dihuang (LWDH) is widely used in traditional Chinese medicine (TCM), but its molecular mechanism about gene interactions is unclear. LWDH genes were extracted from the existing literatures based on text mining technology. To simulate the complex molecular interactions that occur in the whole body, protein-protein interaction networks (PPINs) were constructed and the topological properties of LWDH genes were analyzed. LWDH genes have higher centrality properties and may play important roles in the complex biological network environment. It was also found that the distances within LWDH genes are smaller than expected, which means that the communication of LWDH genes during the biological process is rapid and effectual. At last, a comprehensive network of LWDH genes, including the related drugs and regulatory pathways at both the transcriptional and posttranscriptional levels, was constructed and analyzed. The biological network analysis strategy used in this study may be helpful for the understanding of molecular mechanism of TCM.