Prediction of disease genes using tissue-specified gene-gene network

BMC Systems Biology, Oct 2014

Background Tissue specificity is an important aspect of many genetic diseases in the context of genetic disorders as the disorder affects only few tissues. Therefore tissue specificity is important in identifying disease-gene associations. Hence this paper seeks to discuss the impact of using tissue specificity in predicting new disease-gene associations and how to use tissue specificity along with phenotype information for a particular disease. Methods In order to find out the impact of using tissue specificity for predicting new disease-gene associations, this study proposes a novel method called tissue-specified genes to construct tissues-specific gene-gene networks for different tissue samples. Subsequently, these networks are used with phenotype details to predict disease genes by using Katz method. The proposed method was compared with three other tissue-specific network construction methods in order to check its effectiveness. Furthermore, to check the possibility of using tissue-specific gene-gene network instead of generic protein-protein network at all time, the results are compared with three other methods. Results In terms of leave-one-out cross validation, calculation of the mean enrichment and ROC curves indicate that the proposed approach outperforms existing network construction methods. Furthermore tissues-specific gene-gene networks make a more positive impact on predicting disease-gene associations than generic protein-protein interaction networks. Conclusions In conclusion by integrating tissue-specific data it enabled prediction of known and unknown disease-gene associations for a particular disease more effectively. Hence it is better to use tissue-specific gene-gene network whenever possible. In addition the proposed method is a better way of constructing tissue-specific gene-gene networks.

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Prediction of disease genes using tissue-specified gene-gene network

Ganegoda et al. BMC Systems Biology Prediction of disease genes using tissue-specified gene-gene network Gamage Upeksha Ganegoda 0 JianXin Wang 0 Fang-Xiang Wu 0 1 Min Li 0 0 School of Information Science and Engineering, Central South University , Changsha , China 1 College of Engineering, University of Saskatchewan , 57 Campus Dr., Saskatoon, SK Canada Background: Tissue specificity is an important aspect of many genetic diseases in the context of genetic disorders as the disorder affects only few tissues. Therefore tissue specificity is important in identifying disease-gene associations. Hence this paper seeks to discuss the impact of using tissue specificity in predicting new disease-gene associations and how to use tissue specificity along with phenotype information for a particular disease. Methods: In order to find out the impact of using tissue specificity for predicting new disease-gene associations, this study proposes a novel method called tissue-specified genes to construct tissues-specific gene-gene networks for different tissue samples. Subsequently, these networks are used with phenotype details to predict disease genes by using Katz method. The proposed method was compared with three other tissue-specific network construction methods in order to check its effectiveness. Furthermore, to check the possibility of using tissue-specific gene-gene network instead of generic protein-protein network at all time, the results are compared with three other methods. Results: In terms of leave-one-out cross validation, calculation of the mean enrichment and ROC curves indicate that the proposed approach outperforms existing network construction methods. Furthermore tissues-specific gene-gene networks make a more positive impact on predicting disease-gene associations than generic proteinprotein interaction networks. Conclusions: In conclusion by integrating tissue-specific data it enabled prediction of known and unknown disease-gene associations for a particular disease more effectively. Hence it is better to use tissue-specific genegene network whenever possible. In addition the proposed method is a better way of constructing tissue-specific gene-gene networks. - From IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2013) Shanghai, China. 18-21 December 2013 Introduction The emerging paradigm of network medicine has been proposed to utilize different network-based approaches to predict essential proteins [1-4], identify protein complexes [5-8] and detect candidate genes related to different diseases [9].As methodologies progress, network medicine has the potential to capture the molecular complexity of human disease while offering computational methods to discern how such complexity controls disease manifestations, prognosis, and therapy. Up to now, different types of biological data have been used to study disease related genes and complexes [10-12]. For example, Goh K., et al., [13] constructed a network that consisted of genes associated with the same disease, while Tian W., et al., [14] combined protein and genetic interactions with gene expression correlation. Ulitsky I and Shamir R [15] also combined interactions from published networks and yeast two-hybrid experiments to identify the associations. Analyses of recent research studies, according to CIPHER [16], GeneWalker [17], PRINCE [18] and RWRH [19] highlighted the associations that were derived directly from protein interactions to more distant connections in various ways. Even though genes causing similar diseases lay close to one another in the network, these algorithms did not take into account the fact that the majority of genetic disorders tend to manifest only in a single or a few tissues [13,20]. Tissue specificity is an important aspect of many genetic diseases, reflecting the potentially different roles of proteins and pathways in diverse cell lineages. In the context of genetic disorders, even though the underlying harmful mutation can exist in all the cells in the human body, it most often wreaks havoc only in a few tissues. This tissue selectivity will appear due to the differences in the functionality of the mutated protein within these tissues, its tissue-specific interacting proteins, its abundance and the abundance of its inter-actors. Hence, the purpose of this study is to investigate whether a tissue specific network was a better representation for the actual disease-related tissue, which yields to more accurate prioritizations of the disease-gene associations. Some research has been carried out by constructing tissue specific networks to detect diseases through the Bayesian structure learning algorithms [21]. But Bayesian structure learning algorithms had three major shortcomings, that is, the high computational cost, inefficiency in exploring qualitative knowledge, and the inability to reconstruct phenotype specific gene network. Others [22] analyzed human PPIs in a tissue-specific context, show (...truncated)


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Gamage Ganegoda, JianXin Wang, Fang-Xiang Wu, Min Li. Prediction of disease genes using tissue-specified gene-gene network, BMC Systems Biology, 2014, pp. S3, 8, DOI: 10.1186/1752-0509-8-S3-S3