ArticleBMC bioinformatics2016
Combinatorial identification of DNA methylation patterns over age in the human brain.
Article in BMC bioinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed, 14 citations in OpenAlex.
- Article
- Promoter methylation-mediated repression of UNC5 receptors and the associated clinical significance in human colorectal cancer.Clinical epigenetics · 2021Article
- Genome-wide prediction and prioritization of human aging genes by data fusion: a machine learning approach.BMC genomics · 2019Article
- Analyzing DNA methylation patterns in subjects diagnosed with schizophrenia using machine learning methods.Journal of psychiatric research · 2019Article
- PiiL: visualization of DNA methylation and gene expression data in gene pathways.BMC genomics · 2017Article
- Genome-Wide Epigenetic Characterization of Tissues from Three Germ Layers Isolated from Sheep Fetuses.Frontiers in genetics · 2017Article
- Determination of epigenetic age through DNA methylation of NPTX2 gene using buccal scrapes: A pilot study.Journal of forensic dental sciencesArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDNA methylation plays a key role in developmental processes, which is reflected in changing methylation patterns at specific CpG sites over the lifetime of an individual. The underlying mechanisms are complex and possibly affect multiple genes or entire pathways.
resultsWe applied a multivariate approach to identify combinations of CpG sites that undergo modifications when transitioning between developmental stages. Monte Carlo feature selection produced a list of ranked and statistically significant CpG sites, while rule-based models allowed for identifying particular methylation changes in these sites. Our rule-based classifier reports combinations of CpG sites, together with changes in their methylation status in the form of easy-to-read IF-THEN rules, which allows for identification of the genes associated with the underlying sites.
conclusionWe utilized machine learning and statistical methods to discretize decision class (age) values to get a general pattern of methylation changes over the lifespan. The CpG sites present in the significant rules were annotated to genes involved in brain formation, general development, as well as genes linked to cancer and Alzheimer's disease.
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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.