Evidence map›Paper›PMID 31717838›Full record

ArticleInternational journal of molecular sciences2019

Discrimination of DNA Methylation Signal from Background Variation for Clinical Diagnostics.

Robersy Sanchez, Xiaodong Yang, Thomas Maher, Sally A Mackenzie

Abstract read
In one paragraph

Article in International journal of molecular sciences, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Robersy SanchezDepartments of Biology and Plant Science, The Pennsylvania State University, University Park, PA 16802, USA.ORCID 0000-0002-5246-1453
Xiaodong YangDepartments of Biology and Plant Science, The Pennsylvania State University, University Park, PA 16802, USA.ORCID 0000-0002-4971-2362
Thomas MaherDepartments of Biology and Plant Science, The Pennsylvania State University, University Park, PA 16802, USA.
Sally A MackenzieDepartments of Biology and Plant Science, The Pennsylvania State University, University Park, PA 16802, USA.

Funding

Modeling stress-induced, de novo epiallele architecture in the model Arabidopsis as a tractable entrypointR01GM134056 · NIGMS · PENNSYLVANIA STATE UNIVERSITY, THE · PI MACKENZIE, SALLY A · 2019 to 2022
$1.3M
Bill and Melinda Gates Foundation OPP1088661NIGMS NIH HHS R01 GM134056NIGMS NIH HHS R01 GM134056-01
6 · The paper itself

Abstract

Advances in the study of human DNA methylation variation offer a new avenue for the translation of epigenetic research results to clinical applications. Although current approaches to methylome analysis have been helpful in revealing an epigenetic influence in major human diseases, this type of analysis has proven inadequate for the translation of these advances to clinical diagnostics. As in any clinical test, the use of a methylation signal for diagnostic purposes requires the estimation of an optimal cutoff value for the signal, which is necessary to discriminate a signal induced by a disease state from natural background variation. To address this issue, we propose the application of a fundamental signal detection theory and machine learning approaches. Simulation studies and tests of two available methylome datasets from autism and leukemia patients demonstrate the feasibility of this approach in clinical diagnostics, providing high discriminatory power for the methylation signal induced by disease, as well as high classification performance. Specifically, the analysis of whole biomarker genomic regions could suffice for a diagnostic, markedly decreasing its cost.

Indexed as

DNA MethylationAutistic DisorderComputer SimulationEarly DiagnosisEpigenesis, GeneticFeasibility StudiesFemaleGenetic MarkersHumansLeukemiaMachine LearningPregnancyGenetic Markersautismclinical diagnosticDNA methylationleukemiamachine learningsignal detection

Identifiers

PMID31717838
PMCPMC6862328

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Registered trials

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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.