Title

Modular organization of the human disease genes: a text-based network inference

Authors

Affiliation

¹Department of Biostatistics, School of Science, Anhui Agricultural University, Hefei 230036, China; ²College of Information and Computer science, Anhui Agricultural University, Hefei 230036, China; ³School of life sciences, Anhui Agricultural University,
Hefei 230036, China

Email

zhangshihua@ahau.edu.cn; *Corresponding author

Article Type

Hypothesis

Date

Received July 29, 2015; Revised September 06, 2015; Accepted September 09, 2015; Published September 30, 2015

The analysis of disease phenotype data with genetic information indicated that genes associated with clinically similar diseases tend to be functionally related and work together to perform a specific biological function. Therefore, it is of interest to relate disease phenotype data to mirror modular property implied in the association map of disease genes. Hence, we constructed a textbased human disease gene network (HDGN) by using the phenotypic similarity of their associated disease phenotype records in the OMIM database. Analysis shows that the network is highly modular and it is highly correlated with the physiological classification of genetic diseases. Using a graph clustering algorithm, we found 139 gene modules in the network of 1,865 genes and their gene products (proteins) in these gene modules tend to interact with each other via the computation of PPI intensity. Genes in such gene modules are functionally related and may represent the shared genetic basis of their corresponding diseases. These genes, alone or in combination, could be considered as potential therapeutic targets in future clinical therapy.

disease phenotype, text-mining, modularity, genetic basis.

Citation

Xuan et al. Bioinformation 11(9): 432-436 (2015)
 

Edited by

P Kangueane

ISSN

0973-2063

Publisher

Biomedical Informatics

License

This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.