title: Modular organization of the human disease genes: a text-based network inference.
abstract: 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.
authors.affiliation: Department of Biostatistics, School of Science, Anhui Agricultural University, Hefei 230036, China ; College of Information and Computer science, Anhui Agricultural University, Hefei 230036, China.
authors.forename: Hongdong
authors.initials: H
authors.lastname: Xuan
authors.affiliation: College of Information and Computer science, Anhui Agricultural University, Hefei 230036, China.
authors.forename: Xin
authors.initials: X
authors.lastname: Li
authors.affiliation: School of life sciences, Anhui Agricultural University, Hefei 230036, China.
authors.forename: Shenrong
authors.initials: S
authors.lastname: Ren
authors.affiliation: Department of Biostatistics, School of Science, Anhui Agricultural University, Hefei 230036, China.
authors.forename: Shihua
authors.initials: S
authors.lastname: Zhang
keywords: disease phenotype; genetic basis; modularity; text-mining