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.
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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)