ArticleEntropy (Basel, Switzerland)2022
Cancer Segmentation by Entropic Analysis of Ordered Gene Expression Profiles.
Article in Entropy (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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.
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Who cites it
2 citing papers in PubMed.
- Mapping cancer dynamics from normal tissue to malignancy using N- and T-gene expression markers.Frontiers in systems biology · 2026Article
- N-Gene and T-Gene deregulation networks: a data-driven causal framework for the analysis of gene interventions in cancer.Frontiers in systems biology · 2026Article
Corrections and comments
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Authors and funding
4 authors.
Funding
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Abstract
The availability of massive gene expression data has been challenging in terms of how to cure, process, and extract useful information. Here, we describe the use of entropic measures as discriminating criteria in cancer using the whole data set of gene expression levels. These methods were applied in classifying samples between tumor and normal type for 13 types of tumors with a high success ratio. Using gene expression, ordered by pathways, results in complexity-entropy diagrams. The map allows the clustering of the tumor and normal types samples, with a high success rate for nine of the thirteen, studied cancer types. Further analysis using information distance also shows good discriminating behavior, but, more importantly, allows for discriminating between cancer types. Together, our results allow the classification of tissues without the need to identify relevant genes or impose a particular cancer model. The used procedure can be extended to classification problems beyond the reported results.
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