Integrated epigenomic exposure signature discovery.

The epigenome influences gene regulation and phenotypes in response to exposures. Epigenome assessment can determine exposure history aiding in diagnosis. 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). Signatures were developed for seven exposures including , human immunodeficiency virus, SARS-CoV-2, influenza A (H3N2) virus and vaccinations. ESs differed in the assays and features selected and predictive value. 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.