Disease gene prioritization by integrating tissue-specific molecular networks using a robust multi-network model

Abstract

BackgroundAccurately prioritizing candidate disease genes is an important and challenging problem. Various network-based methods have been developed to predict potential disease genes by utilizing the disease similarity network and molecular networks such as protein interaction or gene co-expression networks. Although successful, a common limitation of the existing methods is that they assume all diseases share the same molecular network and a single generic molecular network is used to predict candidate genes for all diseases. However, different diseases tend to manifest in different tissues, and the molecular networks in different tissues are usually different. An ideal method should be able to incorporate tissue-specific molecular networks for different diseases.ResultsIn this paper, we develop a robust and flexible method to integrate tissue-specific molecular networks for disease gene prioritization. Our method allows each disease to have its own tissue-specific network(s). We formulate the problem of candidate gene prioritization as an optimization problem based on network propagation. When there are multiple tissue-specific networks available for a disease, our method can automatically infer the relative importance of each tissue-specific network. Thus it is robust to the noisy and incomplete network data. To solve the optimization problem, we develop fast algorithms which have linear time complexities in the number of nodes in the molecular networks. We also provide rigorous theoretical foundations for our algorithms in terms of their optimality and convergence properties. Extensive experimental results show that our method can significantly improve the accuracy of candidate gene prioritization compared with the state-of-the-art methods.ConclusionsIn our experiments, we compare our methods with 7 popular network-based disease gene prioritization algorithms on diseases from Online Mendelian Inheritance in Man (OMIM) database. The experimental results demonstrate that our methods recover true associations more accurately than other methods in terms of AUC values, and the performance differences are significant (with paired t-test p-values less than 0.05). This validates the importance to integrate tissue-specific molecular networks for studying disease gene prioritization and show the superiority of our network models and ranking algorithms toward this purpose. The source code and datasets are available at http://nijingchao.github.io/CRstar/.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1317-x) contains supplementary material, which is available to authorized users.

Prioritizing disease genes with an improved dual label propagation framework

“…Conventional cross-validation evaluation strategy, such as leave-one-out cross-validation strategy, does not necessarily reflect the property of novel gene-phenotype associations prediction. To address such cases, we adopt the strategy that has been utilized by [ 21 , 23 , 31 ], i.e. two versions of data are used in the experiments, the Aug-2015 version data are used as validation set to train the model, the newly added data accumulated between Aug-2015 and Dec-2016 are used as test set to measure the performance of the model.…”

Network Medicine in the Age of Biomedical Big Data

“…Efforts to identify specific disease-causing genes, using genomic intervals obtained from linkage mappings or Genome-Wide Association Studies (GWAS), have been undertaken using hybrid heterogeneous networks. These hybrid networks often include a combination of disease-gene networks, generic or tissue-specific molecular networks such as PPIs or GCNs, and prior knowledge of disease similarities (Navlakha and Kingsford, 2010; Moreau and Tranchevent, 2012; Ni et al, 2016). Various network-based tools have been implemented in the gene prioritization problem (Wu et al, 2008; Li and Patra, 2010; Tian et al, 2017).…”

“…Explorations using relative isoform ratios (RNA transcripts from the same genes with different exons removed) and splicing data revealed distinct co-expression relationships unique to the tissues (Saha et al, 2017). Tissue specificity of GCNs have also been assessed in rats (Xiao et al, 2014), humans (Prieto et al, 2008; Xiao et al, 2014; Kogelman et al, 2016; Ni et al, 2016; Farahbod and Pavlidis, 2018), bats (Rodenas-Cuadrado et al, 2015), and plants (Aravind, 2000). Similarly, TCGA data has been analyzed using WGCNA in order to study the system-level properties of prognostic genes (Yang et al, 2014).…”

L-HetNetAligner: A novel algorithm for Local Alignment of Heterogeneous Biological Networks

“…The STRING database 53 uses heterogeneous networks to model functional associations among genes. Other approaches use heterogeneous networks to early detect and to monitor the progression of diseases 52,[54][55][56] .…”