MaxLink: network-based prioritization of genes tightly linked to a disease seed set

Guala, Dimitri; Sjölund, Erik; Sonnhammer, Erik L. L.

doi:10.1093/bioinformatics/btu344

Cited by 34 publications

(24 citation statements)

References 17 publications

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“…MaxLink2223 is a network prioritization algorithm that relies on the Guilt-by-association paradigm24. It scans a gene/protein network of size, | V |, identifies a gene set, S , linked to the supplied set of query genes, Q , and assigns each candidate gene, s ∈ S with a score, corresponding to the number of links to the query set, ML .…”

Section: Methodsmentioning

confidence: 99%

A large-scale benchmark of gene prioritization methods

Guala

Sonnhammer

2017

Sci Rep

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In order to maximize the use of results from high-throughput experimental studies, e.g. GWAS, for identification and diagnostics of new disease-associated genes, it is important to have properly analyzed and benchmarked gene prioritization tools. While prospective benchmarks are underpowered to provide statistically significant results in their attempt to differentiate the performance of gene prioritization tools, a strategy for retrospective benchmarking has been missing, and new tools usually only provide internal validations. The Gene Ontology(GO) contains genes clustered around annotation terms. This intrinsic property of GO can be utilized in construction of robust benchmarks, objective to the problem domain. We demonstrate how this can be achieved for network-based gene prioritization tools, utilizing the FunCoup network. We use cross-validation and a set of appropriate performance measures to compare state-of-the-art gene prioritization algorithms: three based on network diffusion, NetRank and two implementations of Random Walk with Restart, and MaxLink that utilizes network neighborhood. Our benchmark suite provides a systematic and objective way to compare the multitude of available and future gene prioritization tools, enabling researchers to select the best gene prioritization tool for the task at hand, and helping to guide the development of more accurate methods.

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Section: Methodsmentioning

confidence: 99%

A large-scale benchmark of gene prioritization methods

Guala

Sonnhammer

2017

Sci Rep

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“…Currently, there are only few methods that prioritize disease genes at genome-scale 33 . Thus, we select only five of them to compare with GenePANDA, which are Genefriends 25 , Maxlink 24 , Pinta 23 , Candid 34 , and Endeavour 21 . Genefriends and Maxlink, Pinta are network analysis-based methods, while Candid and Endeavour belong to similarity profiling-based methods.…”

Section: Methodsmentioning

confidence: 99%

“…These methods typically predict candidate disease genes by measuring their network characteristics (correlation, connectivity, distance, etc.) to known disease genes from different perspectives, such as Pinta 23 , Maxlink 24 and Genefriends 25 .…”

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confidence: 99%

GenePANDA—a novel network-based gene prioritizing tool for complex diseases

Yin

Chen

et al. 2017

Sci Rep

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Here we describe GenePANDA, a novel network-based tool for prioritizing candidate disease genes. GenePANDA assesses whether a gene is likely a candidate disease gene based on its relative distance to known disease genes in a functional association network. A unique feature of GenePANDA is the introduction of adjusted network distance derived by normalizing the raw network distance between two genes with their respective mean raw network distance to all other genes in the network. The use of adjusted network distance significantly improves GenePANDA’s performance on prioritizing complex disease genes. GenePANDA achieves superior performance over five previously published algorithms for prioritizing disease genes. Finally, GenePANDA can assist in prioritizing functionally important SNPs identified by GWAS.

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“…The distance from a node to a group of nodes in the network may be defined in numerous ways. MaxLink [50], [51] ranks first neighbours of known genes according to a number of direct links to them. In order to reduce hub bias, it takes into account only candidates which have significantly more connection with seed genes than expected by chance.…”

Section: Network Analysismentioning

confidence: 99%

A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases

Zolotareva

Kleine

2019

Journal of Integrative Bioinformatics

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Modern high-throughput experiments provide us with numerous potential associations between genes and diseases. Experimental validation of all the discovered associations, let alone all the possible interactions between them, is time-consuming and expensive. To facilitate the discovery of causative genes, various approaches for prioritization of genes according to their relevance for a given disease have been developed. In this article, we explain the gene prioritization problem and provide an overview of computational tools for gene prioritization. Among about a hundred of published gene prioritization tools, we select and briefly describe 14 most up-to-date and user-friendly. Also, we discuss the advantages and disadvantages of existing tools, challenges of their validation, and the directions for future research.

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MaxLink: network-based prioritization of genes tightly linked to a disease seed set

Cited by 34 publications

References 17 publications

A large-scale benchmark of gene prioritization methods

A large-scale benchmark of gene prioritization methods

GenePANDA—a novel network-based gene prioritizing tool for complex diseases

A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases

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