Integrated epigenomic exposure signature discovery.
Jared SchuetterAngela Minard-SmithBrandon HillJennifer L BeareAlexandria VornholtThomas W BurkeVel MuruganAnthony K SmithThiruppavai ChandrasekaranHiba J ShammaSarah C KahaianKeegan L FillingerMary Anne S AmperWan-Sze ChengYongchao GeMary Catherine GeorgeKristy GuevaraNora Lovette-OkwaraAvinash MahajanNada MarjanovicNatalia MendelevVance G FowlerMicah T McClainClare M MillerSagie MofsowitzVenugopalan D NairGerman NudelmanThomas G EvansFlora CastellinoIrene RamosStas RirakFrederique Ruf-ZamojskiNitish SeenarineAlessandra Soares-ShanoskiSindhu VangetiMital VasoyaXuechen YuElena ZaslavskyLishomwa C NdhlovuMichael J CorleyScott BowlerSteven G DeeksAndrew G LetiziaStuart C SealfonChristopher W WoodsRachel R SpurbeckPublished in: Epigenomics (2024)
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.
Keyphrases
- machine learning
- human immunodeficiency virus
- sars cov
- staphylococcus aureus
- dna methylation
- high throughput
- small molecule
- hepatitis c virus
- deep learning
- healthcare
- air pollution
- public health
- antiretroviral therapy
- mental health
- artificial intelligence
- single cell
- social media
- neural network
- risk assessment
- pseudomonas aeruginosa
- human health