Skip to main content
  • Correspondence
  • Open access
  • Published:

Navigating epigenetic epidemiology publications


Since its beginning more than 75 years ago [1], epigenetics has been an evolving field with growing applications to the study of cancer, aging, and gene expression in response to environmental exposures. The emergence of high-throughput technology for measuring epigenetic markers has enabled population-based studies [2]. The relatively new field of epigenetic epidemiology investigates epigenetic associations from a population perspective for insights into disease risk, prevention, and progression. Unlike genetic variants, epigenetic markers are dynamic, offering epidemiologists a new approach to linking early life and environmental exposures with disease risk [3].

Scientific publications on epigenetic epidemiology have been rapidly increasing in number and variety over the past 20 years. The literature now includes studies of epigenetic markers beyond DNA methylation (DNAm), such as histone modification and non-coding RNA, and consists of a variety of study designs including epigenome-wide association studies (EWAS), candidate gene studies, and clinical trials. Epigenetic markers are investigated as risk factors, such as DNAm in association with type 2 diabetes incidence [4], or outcomes, such as DNAm changes in response to air pollution [5]. The objective of the Epigenetic Epidemiology Publications Database (EEPD) is to offer a user-friendly website to explore the expanding literature in epigenetics, epidemiology, and public health.


In order to better navigate this literature, we created the Epigenetic Epidemiology Publications Database ( We aimed to include population-based studies of epigenetics in association with an exposure or outcome of interest, systematic reviews related to epigenetic associations, and published descriptions of epigenetic resources such as cohorts, protocols, and methods. We aimed to exclude articles on gene expression (e.g., transcriptomics), non-population-based laboratory studies, non-human studies, and non-systematic reviews. We developed an automatic screening process that starts with a PubMed query, followed by application of a machine learning method (support vector machine, SVM) combined with text mining techniques [6]. We assessed the performance of our screening process using a random sample of 1999 manually annotated abstracts. Of these, 285 positive and 114 negative records were used for testing. We estimated area under the curve (AUC) at 96.4% with 90% sensitivity and 90% specificity.

To build the database, records are automatically selected and uploaded daily. Each record is indexed with genes, diseases, and other factors. The system also automatically subsets the data according to 14 special topics (cancer, diabetes, economic evaluation, environmental health, family health history, health equity, heart, lung, blood and sleep disorders, infectious diseases, neurological disorders, pharmacogenomics, primary immune deficiency diseases, rare diseases, and reproductive and child health) by searching the database with specially topic relevant terms.

Description of database

EEPD is a publicly available, web-based application with a user-friendly web interface designed to provide easy access to the scientific literature of epigenetic epidemiology. EEPD provides the opportunity to search for epigenetic epidemiology related literature without having to form a complicated PubMed query. For example, on September 28, 2023, a search for cancer-related epigenetic epidemiology on EEPD provided 8942 records, while a search on PubMed using the terms “cancer” and “epigenetics” and “epidemiology” provided 1198 records, and removing the “epidemiology” term resulted in 17,927 records. Users can type the query of interest in the search box on the top of the page, and the search result will be returned as a list of publications related to the query. To further refine the returning result, the user can use the filtering functions including the following indexed factors such as gene, disease, publication year, publication journal, and study design. The filtering function can be performed continually until the desired result is returned. The final search results can be downloaded as an Excel spreadsheet by clicking on the “Download” button. The database also can be subset to a specific special topic by selecting a dataset for a topic of interest in the dropdown menu in the search bar. With the subset, all the navigation functions can be performed as above.

As of August 18, 2023, the database contains 21,699 PubMed records, indexed with 9378 genes and 1019 disease terms. Since 2000, the annual number of publications has increased markedly (Fig. 1). The most studied topic is Cancer and followed by Rare Diseases. The 5 most frequently indexed disease terms are breast neoplasm, colorectal neoplasms, squamous cell carcinoma, lung neoplasm, and stomach neoplasms, while the 5 most frequently indexed genes are CDKN2A, RASSF1, DNMT1, MGMT, and CDH1.

Fig. 1
figure 1

Epigenetic Epidemiology Publications Database articles per year


EEPD is a novel tool for navigating the epigenetic epidemiology literature, expediting the search for information at the intersection of epigenetics, epidemiology, and public health without labor-intensive screening. EEPD offers investigators an efficient way to gain an overview of available research on a particular topic as well as to find articles relevant to their own projects. The sensitivity of EEPD is not perfect. The search algorithms and indexing processes of PubMed and EEPD have their own limitations in a field that is rapidly evolving and not fully defined. Furthermore, because EEPD relies exclusively on PubMed as a data source, potentially relevant articles not indexed there are not included. Despite these limitations, EEPD can be complementary to traditional search methods for literature review.

In the future, we would hope to expand the data sources for EEPD, using resources such as Scopus and Embase. Further tuning the PubMed search query and EEPD algorithms could also enhance the sensitivity and specificity of data collection and retrieval.

Availability of data and materials

All data in Epigenetic Epidemiology Publications Database can be accessible via the following URL:


  1. Deichmann U. Epigenetics: the origins and evolution of a fashionable topic. Dev Biol. 2016;416(1):249–54.

    Article  CAS  PubMed  Google Scholar 

  2. Moler E, et al. Population Epigenomics: Advancing Understanding of Phenotypic Plasticity, Acclimation, Adaptation and Diseases. In: Rajora OP, ed. Population Genomics: Concepts, Approaches and Applications. Cham: Springer International Publishing; 2019:179–260.

    Google Scholar 

  3. Bakulski K, Fallin MD. Epigenetic epidemiology: promises for public health research. Environ Mol Mutagen. 2014;55:171–83.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Chambers JC, et al. Epigenome-wide association of DNA methylation markers in peripheral blood from Indian Asians and Europeans with incident type 2 diabetes: a nested case-control study. Lancet Diabetes Endocrinol. 2015;3:526–34.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Prunicki M, et al. Air pollution exposure is linked with methylation of immunoregulatory genes, altered immune cell profiles, and increased blood pressure in children. Sci Rep. 2021;11:4067.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Yu W, et al. GAPscreener: an automatic tool for screening human genetic association literature in PubMed using the support vector machine technique. BMC Bioinformatics. 2008;9(205).

    Article  Google Scholar 

Download references


Not applicable.

Author information

Authors and Affiliations



WY designed the infrastructure, developed the application, constructed the database, and drafted the manuscript. ED drafted the manuscript, created the content definition and performed the data analysis. MG oversaw the project, defined the scope of data collection and revised the draft manuscript. YVS provided expert opinion on content selection and data analysis. ED, MFG, ED, MJK and MG were involved in generating the dataset for SVM model training and testing. All authors have read and approved the final manuscript.

Corresponding author

Correspondence to Wei Yu.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yu, W., Drzymalla, E., Gyorfy, M.F. et al. Navigating epigenetic epidemiology publications. Epigenetics Commun. 3, 8 (2023).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: