ArticleFrontiers in genetics2019
Comparative Analysis of Normalization Methods for Network Propagation.
Article in Frontiers in genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 23 citations in OpenAlex.
- NetG2P: Network-based genotype-to-phenotype transformation identifies key signaling crosstalk for prognosis in pan-cancer study.BMC biology · 2026Article
- Assessing individual head and neck squamous cell carcinoma patient response to therapy through integration of functional and genomic data.Scientific reports · 2025Article
- AMEND 2.0: module identification and multi-omic data integration with multiplex-heterogeneous graphs.BMC bioinformatics · 2025Article
- interFLOW: maximum flow framework for the identification of factors mediating the signaling convergence of multiple receptors.NPJ systems biology and applications · 2024Article
- Reaction-diffusion models in weighted and directed connectomes.PLoS computational biology · 2022Article
- Optimizing network propagation for multi-omics data integration.PLoS computational biology · 2021Article
- A network-based machine-learning framework to identify both functional modules and disease genes.Human genetics · 2021Article
- The effect of statistical normalization on network propagation scores.Bioinformatics (Oxford, England) · 2021Article
- Degree Adjusted Large-Scale Network Analysis Reveals Novel Putative Metabolic Disease Genes.Biology · 2021Article
- NetCore: a network propagation approach using node coreness.Nucleic acids research · 2020Article
- Network Diffusion Promotes the Integrative Analysis of Multiple Omics.Frontiers in genetics · 2020Review
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Network propagation is a central tool in biological research. While a number of variants and normalizations have been proposed for this method, each has its own shortcomings and no large scale assessment of those variants is available. Here we propose a novel normalization method for network propagation that is based on evaluating the propagation results against those obtained on randomized networks that preserve node degrees. In this way, our method overcomes potential biases of previous methods. We evaluate its performance on multiple large scale datasets and find that it compares favorably to previous approaches in diverse gene prioritization tasks. We further demonstrate its utility on a focused dataset of telomere length maintenance in yeast. The normalization method is available at http://anat.cs.tau.ac.il/WebPropagate.
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Registered trials
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