Genome-wide inferring gene–phenotype relationship by walking on the heterogeneous network

Li, Yongjin; Patra, J.C.

doi:10.1093/bioinformatics/btq108

Cited by 369 publications

(466 citation statements)

References 41 publications

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“…More importantly, the RWRH algorithm can be extended to use any network of genes/proteins as well as disease similarity one. Indeed, a recent RWRH-based method has used a semantic similarity network of genes instead of the protein interaction network [34] and shown to outperform the original one [32]. We also note that a disease similarity network can be constructed based on shared disease gene [30], shared pathways [35], shared miRNA [36], shared protein complex [37], shared disease ontology [38] and disease comorbidity [39].…”

Section: Introductionmentioning

confidence: 99%

“…This algorithm was then applied to predict disease-associated genes on a heterogeneous network of proteins and disease phenotypes [32]. This network was constructed by integrating a disease similarity network based on text mining algorithms on OMIM records [33] and a protein interaction network.…”

Section: Introductionmentioning

confidence: 99%

“…This network was constructed by integrating a disease similarity network based on text mining algorithms on OMIM records [33] and a protein interaction network. As a result, it was reported that RWRH better exploit "disease module" principle than RWR [22] since then OMIM-based disease similarity network was additionally integrated [32]. More importantly, the RWRH algorithm can be extended to use any network of genes/proteins as well as disease similarity one.…”

Section: Introductionmentioning

confidence: 99%

“…Then, this network was integrated with a gene/protein network by known disease phenotype-gene associations. We compared our method with the one relied on the OMIMbased disease similarity network as in [32,34]. In which, the gene/protein network can be the protein interaction network as in [32], the gene semantic similarity network as in [34] as well as one constructed based on expression profiles of genes.…”

Section: Introductionmentioning

confidence: 99%

“…We compared our method with the one relied on the OMIMbased disease similarity network as in [32,34]. In which, the gene/protein network can be the protein interaction network as in [32], the gene semantic similarity network as in [34] as well as one constructed based on expression profiles of genes. Experimental results show that the performance of our method is better than that based on the OMIM-based disease similarity network irrespective of the gene/protein networks.…”

Section: Introductionmentioning

confidence: 99%

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Ontology-based disease similarity network for disease gene prediction

Dang²

2016

Vietnam J Comput Sci

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Finding underlying molecular mechanisms of diseases is one of the important issues in biomedical research. In which, prediction of novel disease-associated genes is mostly focused. Many methods have been proposed based on biological networks and shown effectively for the problem. These network-based methods are usually relied on a "disease module" principle that functionally similar genes are associated with similar phenotypes or diseases. Among them, methods solely based on gene/protein networks only exploit that principle by structural modules in the gene/protein networks. Meanwhile, others based on integration of these networks with a disease similarity network better exploit the principle and consequently result in higher prediction performance. In these studies, the disease similarity network is extracted from a disease similarity matrix which was calculated using text mining techniques on OMIM records. Considering that diseases have been recently well annotated by human phenotype ontology (i.e., a controlled vocabulary database) and semantic similarity measures can be used to calculate similarities among them. Therefore, it would be more accurate to construct disease similarity network based on semantic similarity measures on phenotype ontol- Experiment results show that the ontology-based disease similarity network much improves the prediction performance compared to the one based on OMIM records, irrespective of gene/protein networks. In addition, we show ability of our method in predicting novel Alzheimer's disease-associated genes, in which 19 out of top 100 ranked candidate genes are supported with evidences from literature.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Ontology-based disease similarity network for disease gene prediction

Dang²

2016

Vietnam J Comput Sci

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Predicting disease‐associated genes: Computational methods, databases, and evaluations

Luo

Chen

Liao

et al. 2020

WIREs Data Min & Knowl

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Complex diseases are associated with a set of genes (called disease genes), the identification of which can help scientists uncover the mechanisms of diseases and develop new drugs and treatment strategies. Due to the huge cost and time of experimental identification techniques, many computational algorithms have been proposed to predict disease genes. Although several review publications in recent years have discussed many computational methods, some of them focus on cancer driver genes while others focus on biomolecular networks, which only cover a specific aspect of existing methods. In this review, we summarize existing methods and classify them into three categories based on their rationales. Then, the algorithms, biological data, and evaluation methods used in the computational prediction are discussed. Finally, we highlight the limitations of existing methods and point out some future directions for improving these algorithms. This review could help investigators understand the principles of existing methods, and thus develop new methods to advance the computational prediction of disease genes. This article is categorized under: Technologies > Machine Learning Technologies > Prediction Algorithmic Development > Biological Data Mining

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Recent approaches to the prioritization of candidate disease genes

Doncheva

Kacprowski

Albrecht

2012

WIREs Mechanisms of Disease

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Many efforts are still devoted to the discovery of genes involved with specific phenotypes, in particular, diseases. High-throughput techniques are thus applied frequently to detect dozens or even hundreds of candidate genes. However, the experimental validation of many candidates is often an expensive and time-consuming task. Therefore, a great variety of computational approaches has been developed to support the identification of the most promising candidates for follow-up studies. The biomedical knowledge already available about the disease of interest and related genes is commonly exploited to find new gene-disease associations and to prioritize candidates. In this review, we highlight recent methodological advances in this research field of candidate gene prioritization. We focus on approaches that use network information and integrate heterogeneous data sources. Furthermore, we discuss current benchmarking procedures for evaluating and comparing different prioritization methods.

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Genome-wide inferring gene–phenotype relationship by walking on the heterogeneous network

Abstract: The MATLAB code of the program is available at http://www3.ntu.edu.sg/home/aspatra/research/Yongjin_BI2010.zip.

Cited by 369 publications

References 41 publications

Ontology-based disease similarity network for disease gene prediction

Ontology-based disease similarity network for disease gene prediction

Predicting disease‐associated genes: Computational methods, databases, and evaluations

Recent approaches to the prioritization of candidate disease genes

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