Table 3.
Network-based predictions of disease-related genes as biomarkers
|
Name and additional description, website | References |
|
new disease-related proteins are predicted by their structural similarity to known disease-related proteins | Vilar et al., 2009 |
|
new disease-related genes are predicted by their interactome neighborhood | Krauthammer et al., 2004; Chen et al., 2006a; Oti et al., 2006; Xu & Li, 2006 |
|
measures the neighborhood association in both the interactome and disease similarity networks and iteratively calculates the similarity of the node to diseases | Guo et al., 2011 |
|
calculates a semantic similarity score between gene ontology terms as well as human genes associated with them | Jiang et al., 2011 |
|
constructs an integrative network and predicts candidate genes by their network closeness to known disease-related genes; Prioritizer: http://129.125.135.180/prioritizer | Franke et al., 2006 |
|
uses a maximum expectation gene cover algorithm finding small gene sets to predict associated new disease-related genes | Karni et al., 2009 |
|
new disease-related genes are predicted by their interactome closeness to known disease-related proteins; Genes2Networks: http://actin.pharm.mssm.edu/genes2networks | Berger et al., 2007 |
|
new disease-related genes are predicted by their association to previously known disease-related genes at protein-protein domains affected by the disease-associated mutations of the known disease related gene | Sharma et al., 2010a; Song & Lee, 2012 |
|
new disease-related genes are predicted by their association to previously known disease-related genes at 3D modeled protein-protein interfaces affected by the disease-associated mutations of the known disease related gene | Wang et al., 2012b |
|
new disease-related genes are predicted by their common protein-protein interaction network module with previous disease-related genes closeness of unrelated proteins is calculated in the interactome from protein products of disease-related genes, and compared with phenotype similarity profile: large closeness marks a potential new disease-related gene; CIPHER: http://rulai.cshl.edu/tools/cipher | Navlakha & Kingsford, 2010; Wu et al., 2008 |
|
random walks in the interactome are started from protein products of disease-related genes: frequent visits of a previously unrelated protein mark a potential new disease-related gene; Cytoscape plug-in GPEC: http://sourceforge.net/p/gpec | Kohler et al., 2008; Chen et al., 2009b; Le & Kwon, 2012 |
|
iterative steps of information flow from disease-related and between interacting proteins: after convergence a large flow of a previously unrelated protein marks potential new disease-related gene; Cytoscape plug-in PRINCIPLE/PRINCE: http://www.cs.tau.ac.il/~bnet/software/PrincePlugin | Vanunu et al., 2010; Gottlieb et al., 2011b |
|
random walk in both the interactome and the disease networks: number of frequent visits marks candidate genes | Li & Patra, 2010 |
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after alignment of the interactome and disease networks finds high scoring subnetworks (bi-modules); candidate genes have the highest scoring bi-modules | Wu et al., 2009a |
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statistically corrects random walk- based prediction with the degree distribution of the network; DADA: http://compbio.case.edu/dada | Erten et al., 2011a |
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calculates neighborhood similarity in the interactome and prioritizes candidate genes; VAVIEN: http://diseasegenes.org | Erten et al., 2011b |
|
calculates expression weighted neighborhood similarity (using Katz centrality or other methods) in the interactome | Zhao et al., 2011b; Wu et al., 2012 |
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calculates data-type weighted centrality in the integrated network and uses it as a rank of candidate genes | Gudivala et al., 2008 |
|
constructs candidate protein complexes in a virtual pull-down experiment, and scores candidates by measuring the similarity between the phenotype in the complex and disease phenotype | Lage et al., 2007 |
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calculates genetic linkage analysis of connected clusters in a text mining-derived direct interaction network | Iossifov et al., 2008 |
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predict deleterious SNPs and disease genes using the random forest learning method, uses interactomes and deleterious SNPs to predict disease-related genes by random forest learning | Care et al., 2009 |
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a Cytoscape plug-in to construct an integrative network of diseases, associated genes, drugs and tissues; iCTNet: http://www.cs.queensu.ca/ictnet | Wang et al., 2011b |
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integrative methods using similarities of neighbors or shortest paths in multiple data sources including interactomes; Endeavour: http://esat.kuleuven.be/endeavour; Phenopred: http://www.phenopred.org | Radijovac et al., 2008; Tranchevent et al., 2008; Linghu et al., 2009; Costa et al., 2010 |
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Calculates rank coherences between the integrated network characteristic to the target disease and unrelated diseases; rcNet: http://phegenex.cs.umn.edu/Nano | Hwang et al., 2011 |
The Table summarizes methods using networks as data representations. We excluded those methods, like neural network or Bayesian network-based methods, which decipher associations between various, not network-assembled data. Several methods are included in the excellent reviews of Wang et al. (2011a) and Doncheva et al. (2012a).