NetRanker: A network-based gene ranking tool using protein-protein interaction and gene expression data

Jadamba, Erkhembayar; Cho, Seongbeom; Shin, Moo Whan

doi:10.1007/s13206-015-9407-9

Cited by 5 publications

(3 citation statements)

References 28 publications

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“…NCBI and other common databases, such as HPRD, MeSH, etc., can be used as data sources for hybrid methods. A few examples in this category include PrixFixe [45], ENDEAVOR [46], GeneMANIA [47], Gene Prospector [48] and DAPPLE [49], NetRanker [50], GeneTIER [51].…”

Section: Introductionmentioning

confidence: 99%

A novel candidate disease gene prioritization method using deep graph convolutional networks and semi-supervised learning

Azadifar

Ahmadi

2022

BMC Bioinformatics

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Background Selecting and prioritizing candidate disease genes is necessary before conducting laboratory studies as identifying disease genes from a large number of candidate genes using laboratory methods, is a very costly and time-consuming task. There are many machine learning-based gene prioritization methods. These methods differ in various aspects including the feature vectors of genes, the used datasets with different structures, and the learning model. Creating a suitable feature vector for genes and an appropriate learning model on a variety of data with different and non-Euclidean structures, including graphs, as well as the lack of negative data are very important challenges of these methods. The use of graph neural networks has recently emerged in machine learning and other related fields, and they have demonstrated superior performance for a broad range of problems. Methods In this study, a new semi-supervised learning method based on graph convolutional networks is presented using the novel constructing feature vector for each gene. In the proposed method, first, we construct three feature vectors for each gene using terms from the Gene Ontology (GO) database. Then, we train a graph convolution network on these vectors using protein–protein interaction (PPI) network data to identify disease candidate genes. Our model discovers hidden layer representations encoding in both local graph structure as well as features of nodes. This method is characterized by the simultaneous consideration of topological information of the biological network (e.g., PPI) and other sources of evidence. Finally, a validation has been done to demonstrate the efficiency of our method. Results Several experiments are performed on 16 diseases to evaluate the proposed method's performance. The experiments demonstrate that our proposed method achieves the best results, in terms of precision, the area under the ROC curve (AUCs), and F1-score values, when compared with eight state-of-the-art network and machine learning-based disease gene prioritization methods. Conclusion This study shows that the proposed semi-supervised learning method appropriately classifies and ranks candidate disease genes using a graph convolutional network and an innovative method to create three feature vectors for genes based on the molecular function, cellular component, and biological process terms from GO data.

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Section: Introductionmentioning

confidence: 99%

A novel candidate disease gene prioritization method using deep graph convolutional networks and semi-supervised learning

Azadifar

Ahmadi

2022

BMC Bioinformatics

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“…Use of biological networks such as Protein-Protein Interaction network (PPIN) and network analysis is a commonly used approach for the uncovering of disease genes [18]. The PPIN is an important biological network widely used in areas from drug target prediction, predication of protein complexes, functions, identification of essential genes and network motif discovery [19].…”

Section: Introductionmentioning

confidence: 99%

An Outranking Approach for Gene Prioritization Using Multinetworks

Noriega¹,

López²,

Browne³

et al. 2021

IJCIS

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High-throughput experimental techniques such as genome-wide association studies have been instrumental in the identification of disease-associated genes. These methods often produce large lists of disease candidate genes which are time-consuming and expensive to experimentally validate. Computational gene prioritization methods are required to identify relevant genes from a larger pool of candidates. Research has shown that the integration of diverse "omic" evidence can reduce the candidategene search space. In this paper we present a general framework that integrates "omic" data using a multinetwork approach and topological analysis to prioritize disease-candidate genes. Specifically, we propose a data integration method within a multicriteria decision analysis context using aggregation mechanisms based on decision rules identifying positive and negative criteria for judging gene-candidates ranks. The proposed multinetwork disease gene prioritization method is applied to the prioritization of disease genes in ovarian cancer progression. Using this approach we uncovered known ovarian cancer genes GSTA1, ERBB2, IL1A, MAGEB2, along with significantly enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways ErbB signaling and pathways in cancer. Relatively high predictive performance (area under Receiver Operating Characteristic [ROC] curve 0.704) was observed when classifying epithelial ovarian high-grade serous carcinoma cancer early and late stage RNA-Seq expression profiles from individuals using 10-fold cross-validation.

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“…In this case, while the data can be easily kept up-to-date, the analysis approach is usually built around those specific types of data. And lastly, although some tools can work on user-provided datasets, they are only capable of using some pre-defined types of information [ 13 , 14 ]. Some notable examples of such tools include NetworkPrioritizer [ 15 ] and iCTNet [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

Arete – candidate gene prioritization using biological network topology with additional evidence types

2017

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BackgroundRefinement of candidate gene lists to select the most promising candidates for further experimental verification remains an essential step between high-throughput exploratory analysis and the discovery of specific causal genes. Given the qualitative and semantic complexity of biological data, successfully addressing this challenge requires development of flexible and interoperable solutions for making the best possible use of the largest possible fraction of all available data.ResultsWe have developed an easily accessible framework that links two established network-based gene prioritization approaches with a supporting isolation forest-based integrative ranking method. The defining feature of the method is that both topological information of the biological networks and additional sources of evidence can be considered at the same time. The implementation was realized as an app extension for the Cytoscape graph analysis suite, and therefore can further benefit from the synergy with other analysis methods available as part of this system.ConclusionsWe provide efficient reference implementations of two popular gene prioritization algorithms – DIAMOnD and random walk with restart for the Cytoscape system. An extension of those methods was also developed that allows outputs of these algorithms to be combined with additional data. To demonstrate the utility of our software, we present two example disease gene prioritization application cases and show how our tool can be used to evaluate these different approaches.Electronic supplementary materialThe online version of this article (doi:10.1186/s13040-017-0141-9) contains supplementary material, which is available to authorized users.

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NetRanker: A network-based gene ranking tool using protein-protein interaction and gene expression data

Cited by 5 publications

References 28 publications

A novel candidate disease gene prioritization method using deep graph convolutional networks and semi-supervised learning

A novel candidate disease gene prioritization method using deep graph convolutional networks and semi-supervised learning

An Outranking Approach for Gene Prioritization Using Multinetworks

Arete – candidate gene prioritization using biological network topology with additional evidence types

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