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Open AccessArticle

Discrimination of DNA Methylation Signal from Background Variation for Clinical Diagnostics

Departments of Biology and Plant Science, The Pennsylvania State University, University Park, PA 16802, USA
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Int. J. Mol. Sci. 2019, 20(21), 5343; https://doi.org/10.3390/ijms20215343
Received: 3 September 2019 / Revised: 9 October 2019 / Accepted: 24 October 2019 / Published: 27 October 2019
(This article belongs to the Collection Technical Pitfalls and Biases in Molecular Biology)
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. View Full-Text
Keywords: DNA methylation; signal detection; machine learning; leukemia; autism; clinical diagnostic DNA methylation; signal detection; machine learning; leukemia; autism; clinical diagnostic
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Sanchez, R.; Yang, X.; Maher, T.; Mackenzie, S.A. Discrimination of DNA Methylation Signal from Background Variation for Clinical Diagnostics. Int. J. Mol. Sci. 2019, 20, 5343.

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