Inferring disease and gene set associations with rank coherence in networks

Hwang, Taesung; Zhang, Wei; Xie, Minzhu; Liu, Jinfeng; Kuang, Rui

doi:10.1093/bioinformatics/btr463

Cited by 36 publications

(34 citation statements)

References 27 publications

(48 reference statements)

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“…As Algorithm 1 shows, these processes are repeatedly applied to each network in every path from the query network to the target network. Values propagated from the query network eventually reach the target network and are then compared with values propagated from the target nodes by correlation [16]. …”

Section: Resultsmentioning

confidence: 99%

ProphTools: general prioritization tools for heterogeneous biological networks

et al. 2017

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BackgroundNetworks have been proven effective representations for the analysis of biological data. As such, there exist multiple methods to extract knowledge from biological networks. However, these approaches usually limit their scope to a single biological entity type of interest or they lack the flexibility to analyze user-defined data.ResultsWe developed ProphTools, a flexible open-source command-line tool that performs prioritization on a heterogeneous network. ProphTools prioritization combines a Flow Propagation algorithm similar to a Random Walk with Restarts and a weighted propagation method. A flexible model for the representation of a heterogeneous network allows the user to define a prioritization problem involving an arbitrary number of entity types and their interconnections. Furthermore, ProphTools provides functionality to perform cross-validation tests, allowing users to select the best network configuration for a given problem. ProphTools core prioritization methodology has already been proven effective in gene-disease prioritization and drug repositioning. Here we make ProphTools available to the scientific community as flexible, open-source software and perform a new proof-of-concept case study on long noncoding RNAs (lncRNAs) to disease prioritization.ConclusionsProphTools is robust prioritization software that provides the flexibility not present in other state-of-the-art network analysis approaches, enabling researchers to perform prioritization tasks on any user-defined heterogeneous network. Furthermore, the application to lncRNA-disease prioritization shows that ProphTools can reach the performance levels of ad hoc prioritization tools without losing its generality.

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Section: Resultsmentioning

confidence: 99%

ProphTools: general prioritization tools for heterogeneous biological networks

et al. 2017

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“…We report the AUC values for up to 50, 100, 300, 500, 700 and 1000 false positives. These values are effective to estimate the prediction accuracy of each method for top ranked genes and have been widely used to evaluate gene prioritization methods [13, 22, 31]. For example, the average AUC50 is large if many test genes are ranked highly among the top 50 of the ranking list and the average AUC50 is 1 if all test genes are ranked first in their respective validation runs.…”

Section: Resultsmentioning

confidence: 99%

“…The similarities are calculated based on the medical subject headings description in the Online Mendelian Inheritance in Man (OMIM) database [30]. Following the approach in [7, 11, 31], we construct a k -nearest-neighbor graph of the disease similarity network with k =5, a good choice that has been evaluated by earlier studies [7, 11, 31]. By doing so, there are 21006 edges in the disease similarity network.…”

Section: Methodsmentioning

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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“…We focus our study on two recent methods: rcNet (Hwang et al, 2011) and domainRBF ) since they outperform previous methods. Despite their good performance, these methods have clear limitations.…”

Section: Motivation and Objectivesmentioning

confidence: 99%