Integrated epigenomic exposure signature discovery.

IF 3 4区医学 Q2 GENETICS & HEREDITY

Epigenomics Pub Date : 2024-01-01 Epub Date: 2024-09-03 DOI:10.1080/17501911.2024.2375187

Jared Schuetter, Angela Minard-Smith, Brandon Hill, Jennifer L Beare, Alexandria Vornholt, Thomas W Burke, Vel Murugan, Anthony K Smith, Thiruppavai Chandrasekaran, Hiba J Shamma, Sarah C Kahaian, Keegan L Fillinger, Mary Anne S Amper, Wan-Sze Cheng, Yongchao Ge, Mary Catherine George, Kristy Guevara, Nora Lovette-Okwara, Avinash Mahajan, Nada Marjanovic, Natalia Mendelev, Vance G Fowler, Micah T McClain, Clare M Miller, Sagie Mofsowitz, Venugopalan D Nair, German Nudelman, Thomas G Evans, Flora Castellino, Irene Ramos, Stas Rirak, Frederique Ruf-Zamojski, Nitish Seenarine, Alessandra Soares-Shanoski, Sindhu Vangeti, Mital Vasoya, Xuechen Yu, Elena Zaslavsky, Lishomwa C Ndhlovu, Michael J Corley, Scott Bowler, Steven G Deeks, Andrew G Letizia, Stuart C Sealfon, Christopher W Woods, Rachel R Spurbeck

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引用次数: 0

Abstract

Aim: The epigenome influences gene regulation and phenotypes in response to exposures. Epigenome assessment can determine exposure history aiding in diagnosis.Materials & methods: Here we developed and implemented a machine learning algorithm, the exposure signature discovery algorithm (ESDA), to identify the most important features present in multiple epigenomic and transcriptomic datasets to produce an integrated exposure signature (ES).Results: Signatures were developed for seven exposures including Staphylococcus aureus, human immunodeficiency virus, SARS-CoV-2, influenza A (H3N2) virus and Bacillus anthracis vaccinations. ESs differed in the assays and features selected and predictive value.Conclusion: Integrated ESs can potentially be utilized for diagnosis or forensic attribution. The ESDA identifies the most distinguishing features enabling diagnostic panel development for future precision health deployment.

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综合表观基因组暴露特征发现。

目的：表观基因组影响基因调控和表型对暴露的反应。材料与方法：在此，我们开发并实施了一种机器学习算法--暴露特征发现算法（ESDA），以识别多个表观基因组和转录组数据集中存在的最重要特征，从而生成综合暴露特征（ES）：为七种暴露开发了特征，包括金黄色葡萄球菌、人类免疫缺陷病毒、SARS-CoV-2、甲型流感（H3N2）病毒和炭疽杆菌疫苗接种。ES 在所选检测方法和特征以及预测价值方面存在差异：综合 ES 有可能用于诊断或法医归因。ESDA确定了最显著的特征，有助于为未来的精准健康部署开发诊断面板。

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来源期刊

Epigenomics GENETICS & HEREDITY-

CiteScore

5.80

自引率

2.60%

发文量

审稿时长

>12 weeks

期刊介绍： Epigenomics provides the forum to address the rapidly progressing research developments in this ever-expanding field; to report on the major challenges ahead and critical advances that are propelling the science forward. The journal delivers this information in concise, at-a-glance article formats – invaluable to a time constrained community. Substantial developments in our current knowledge and understanding of genomics and epigenetics are constantly being made, yet this field is still in its infancy. Epigenomics provides a critical overview of the latest and most significant advances as they unfold and explores their potential application in the clinical setting.