ArticleNetwork science (Cambridge University Press)2022
Generating weighted and thresholded gene coexpression networks using signed distance correlation.
Article in Network science (Cambridge University Press), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Formation and transfer patterns of key aroma compounds in cigar tobacco based on SBSE-GC-O-MS, pyrolysis-GC-O-MS and flue gas analysis correlation studies.Frontiers in chemistry · 2026Article
- Clinically validated graphical approaches identify hepatosplenic multimorbidity in individuals at risk of schistosomiasis.Royal Society open science · 2025Article
- Accelerating crop improvement via integration of transcriptome-based network biology and genome editing.Planta · 2025Review
- GeCoNet-Tool: a software package for gene co-expression network construction and analysis.BMC bioinformatics · 2023Article
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5 authors.
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Abstract
Even within well-studied organisms, many genes lack useful functional annotations. One way to generate such functional information is to infer biological relationships between genes or proteins, using a network of gene coexpression data that includes functional annotations. Signed distance correlation has proved useful for the construction of unweighted gene coexpression networks. However, transforming correlation values into unweighted networks may lead to a loss of important biological information related to the intensity of the correlation. Here we introduce a principled method to construct
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