ArticleBMC proceedings2018
A deep neural network based regression model for triglyceride concentrations prediction using epigenome-wide DNA methylation profiles.
Article in BMC proceedings, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
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Who cites it
3 citing papers in PubMed.
- MethylNet: an automated and modular deep learning approach for DNA methylation analysis.BMC bioinformatics · 2020Article
- iPromoter-5mC: A Novel Fusion Decision Predictor for the Identification of 5-Methylcytosine Sites in Genome-Wide DNA Promoters.Frontiers in cell and developmental biology · 2020Article
- Data mining and machine learning approaches for the integration of genome-wide association and methylation data: methodology and main conclusions from GAW20.BMC genetics · 2018Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
backgroundEpigenetic modification has an effect on gene expression under the environmental alteration, but it does not change corresponding genome sequence. DNA methylation (DNAm) is one of the important epigenetic mechanisms. DNAm variations could be used as epigenetic markers to predict and account for the change of many human phenotypic traits, such as cancer, diabetes, and high blood pressure. In this study, we built deep neural network (DNN) regression models to account for interindividual variation in triglyceride concentrations measured at different visits of peripheral blood samples using epigenome-wide DNAm profiles.
resultsWe used epigenome-wide DNAm profiles of before and after medication interventions (called
conclusionsWe demonstrated the superiority of our proposed DNN models over the SVM model for predicting triglyceride concentrations. This study also suggests that the DNN approach has advantages over other traditional machine-learning methods to model high-dimensional epigenome-wide DNAm data and other genomic data.
Identifiers
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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.