Prioritizing Disease Genes by Bi-Random Walk

Xie, Minzhu; Hwang, Taesung; Kuang, Rui

doi:10.1007/978-3-642-30220-6_25

Cited by 48 publications

(49 citation statements)

References 21 publications

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“…Our problem formulation generalizes the existing label propagation methods designed for using a single generic molecular network [12, 13] to NoN. Important symbols used in this paper are summarized in Table 1.…”

Section: Methodsmentioning

confidence: 99%

“…The first two criteria focus on ranking scores within individual molecular networks. They are commonly used in previous network prorogation methods [12, 13, 26]. The third criterion is specific to the NoN model.…”

Section: Methodsmentioning

confidence: 99%

“…Note that the well known label propagation methods [12, 13, 26] only optimize the within-network smoothness and seed preference criteria for a single network, i.e., Θ within . In our method, we generalize it to multiple networks and further introduce the cross-network consistency criterion Θ cross .…”

Section: Methodsmentioning

confidence: 99%

“…We use two versions of disease-gene associations that are frequently used in previous studies [13, 31]. These two sets of associations are obtained from OMIM on May 2007 and May 2010, respectively.…”

Section: Methodsmentioning

confidence: 99%

“…Such a network model is usually referred to as the heterogeneous network model because of the heterogeneity between the disease network and molecular network [7]. Based on this heterogeneous network model, various approaches have been proposed, including regression [8], network alignment [9], random walk [7, 10], maximum flow [11], label propagation [12, 13], and supervised link prediction [10, 14].

Fig.…”

Section: Introductionmentioning

confidence: 99%

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

Koyutürk

Tong

et al. 2016

BMC Bioinformatics

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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.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Fig.…”

Section: Introductionmentioning

confidence: 99%

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

Koyutürk

Tong

et al. 2016

BMC Bioinformatics

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Link Prediction in Multi-layer Networks and Its Application to Drug Design

Koptelov

Zimmermann

Crémilleux

2018

Advances in Intelligent Data Analysis XVII

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Search of valid drug candidates for a given target is a vital part of modern drug discovery. Since the problem was established, a number of approaches have been proposed that augment interaction networks with, typically, two compound/target similarity networks. In this work we propose a method capable of using an arbitrary number of similarity or interaction networks. We adapt an existing method for random walks on heterogeneous networks and show that adding additional networks improves prediction quality.

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