ArticleiScience2021
Spatial constrains and information content of sub-genomic regions of the human genome.
Article in iScience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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.
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.
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Who cites it
6 citing papers in PubMed.
- Inflammation-related lncRNAs in the regulation of kidney injuries; special emphasis on novel lncRNA-based delivery platforms.Naunyn-Schmiedeberg's archives of pharmacology · 2026Review
- Quantifying attention in children with intellectual and developmental disabilities through multicenter electrooculogram signal analysis.Scientific reports · 2024Article
- Bioinformatics tools for the sequence complexity estimates.Biophysical reviews · 2023Review
- Article
- A Bayesian Analysis of Plant DNA Length Distribution viaEntropy (Basel, Switzerland) · 2022Article
- A Novel Method for Colorectal Cancer Screening Based on Circulating Tumor Cells and Machine Learning.Entropy (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Complexity metrics and machine learning (ML) models have been utilized to analyze the lengths of segmental genomic entities of DNA sequences (exonic, intronic, intergenic, repeat, unique) with the purpose to ask questions regarding the segmental organization of the human genome within the size distribution of these sequences. For this we developed an integrated methodology that is based upon the reconstructed phase space theorem, the non-extensive statistical theory of Tsallis, ML techniques, and a technical index, integrating the generated information, which we introduce and named complexity factor (COFA). Our analysis revealed that the size distribution of the genomic regions within chromosomes are not random but follow patterns with characteristic features that have been seen through its complexity character, and it is part of the dynamics of the whole genome. Finally, this picture of dynamics in DNA is recognized using ML tools for clustering, classification, and prediction with high accuracy.
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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.