ArticleInternational journal of computational biology and drug design2014
Differential Shannon entropy and differential coefficient of variation: alternatives and augmentations to differential expression in the search for disease-related genes.
Article in International journal of computational biology and drug design, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- Exploring cell-to-cell variability and functional insights through differentially variable gene analysis.NPJ systems biology and applications · 2025Article
- Quantifying transcriptome diversity: a review.Briefings in functional genomics · 2024Review
- Detection of genes with differential expression dispersion unravels the role of autophagy in cancer progression.PLoS computational biology · 2023Article
- Article
- CEPS: An Open Access MATLAB Graphical User Interface (GUI) for the Analysis of Complexity and Entropy in Physiological Signals.Entropy (Basel, Switzerland) · 2021Article
- Using RNentropy to Detect Significant Variation in Gene Expression Across Multiple RNA-Seq or Single-Cell RNA-Seq Samples.Methods in molecular biology (Clifton, N.J.) · 2021Article
- Article
- RNentropy: an entropy-based tool for the detection of significant variation of gene expression across multiple RNA-Seq experiments.Nucleic acids research · 2018Article
- EntropyExplorer: an R package for computing and comparing differential Shannon entropy, differential coefficient of variation and differential expression.BMC research notes · 2015Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Differential expression has been a standard tool for analysing case-control transcriptomic data since the advent of microarray technology. It has proved invaluable in characterising the molecular mechanisms of disease. Nevertheless, the expression profile of a gene across samples can be perturbed in ways that leave the expression level unaltered, while a biological effect is nonetheless present. This paper describes and analyses differential Shannon entropy and differential coefficient of variation, two alternate techniques for identifying genes of interest. Ontological analysis across 16 human disease datasets demonstrates that these alternatives are effective at identifying disease-related genes not found by mere differential expression alone. Because the two alternate techniques are based on somewhat different mathematical formulations, they tend to produce somewhat different gene lists. Moreover, each may pinpoint genes completely overlooked by the other. Thus, measures of entropy and variation can be used to replace or better yet augment standard differential expression computations.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.