ArticleGenomics data2016
Analysis of distribution of DNA methylation in kidney-renal-clear-cell-carcinoma specific genes using entropy.
Article in Genomics data, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed, 6 citations in OpenAlex.
- Methylation entropy as a novel dimension in liquid biopsy: enhanced multimodal framework for cancer detection and tissue-of-origin classification.Journal of translational medicine · 2026Article
- Epigenomic subtypes and prognostic methylation signatures in clear cell renal cell carcinoma.Clinical epigenetics · 2026Article
- The Role of DNA Methylation in Renal Cell Carcinoma.Molecular diagnosis & therapy · 2018Review
- Analysis of healthy and tumour DNA methylation distributions in kidney-renal-clear-cell-carcinoma using Kullback-Leibler and Jensen-Shannon distance measures.IET systems biology · 2017Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
DNA Methylation is an epigenetic phenomenon in which methyl groups are added to the cytosines, thereby altering the physio-chemical properties of the DNA region and influencing gene expression. Aberrant DNA methylation in a set of genes or across the genome results in many epigenetic diseases including cancer. In this paper, we use entropy to analyze the extent and distribution of DNA methylation in Tumor Suppressor Genes (TSG's) and Oncogenes related to a specific type of cancer (viz.) KIRC (Kidney-renal-clear-cell-carcinoma). We apply various mathematical transformations to enhance the different regions in DNA methylation distribution and compare the resultant entropies for healthy and tumor samples. We also obtain the sensitivity and specificity of classification for the different mathematical transformations. Our findings show that it is not just the measure of methylation, but the distribution of the methylation levels in the genes that are significant in cancer.
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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.