ArticleMethods (San Diego, Calif.)2018
Network module identification-A widespread theoretical bias and best practices.
Article in Methods (San Diego, Calif.), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Ant colony optimization for the identification of dysregulated gene subnetworks from expression data.BMC bioinformatics · 2024Article
- NetMix2: A Principled Network Propagation Algorithm for Identifying Altered Subnetworks.Journal of computational biology : a journal of computational molecular cell biology · 2022Article
- TopoFun: a machine learning method to improve the functional similarity of gene co-expression modules.NAR genomics and bioinformatics · 2021Article
- Construction and characterization of rectal cancer-related lncRNA-mRNA ceRNA network reveals prognostic biomarkers in rectal cancer.IET systems biology · 2021Article
- NetMix: A Network-Structured Mixture Model for Reduced-Bias Estimation of Altered Subnetworks.Journal of computational biology : a journal of computational molecular cell biology · 2021Article
- Boosting GWAS using biological networks: A study on susceptibility to familial breast cancer.PLoS computational biology · 2021Article
- DOMINO: a network-based active module identification algorithm with reduced rate of false calls.Molecular systems biology · 2021Article
- Molecular networks in Network Medicine: Development and applications.Wiley interdisciplinary reviews. Systems biology and medicine · 2020Review
- pathfindR: An R Package for Comprehensive Identification of Enriched Pathways in Omics Data Through Active Subnetworks.Frontiers in genetics · 2019Article
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Authors and funding
3 authors.
Funding
Abstract
Biological processes often manifest themselves as coordinated changes across modules, i.e., sets of interacting genes. Commonly, the high dimensionality of genome-scale data prevents the visual identification of such modules, and straightforward computational search through a set of known pathways is a limited approach. Therefore, tools for the data-driven, computational, identification of modules in gene interaction networks have become popular components of visualization and visual analytics workflows. However, many such tools are known to result in modules that are large, and therefore hard to interpret biologically. Here, we show that the empirically known tendency towards large modules can be attributed to a statistical bias present in many module identification tools, and discuss possible remedies from a mathematical perspective. In the current absence of a straightforward practical solution, we outline our view of best practices for the use of the existing tools.
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Registered trials
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