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Traditional analyses that associate DNAm alterations with phenotypes suffer from multiple hypothesis testing and multi-collinearity due to the high-dimensional, continuous, interacting and non-linear nature of the data. Deep learning analyses have shown much promise to study disease heterogeneity. DNAm deep learning approaches have not yet been formalized into user-friendly frameworks for execution, training, and interpreting models. Here, we describe MethylNet, a DNAm deep learning method that can construct embeddings, make predictions, generate new data, and uncover unknown heterogeneity with minimal user supervision.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The results of our experiments indicate that MethylNet can study cellular differences, grasp higher order information of cancer sub-types, estimate age and capture factors associated with smoking in concordance with known differences.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The ability of MethylNet to capture nonlinear interactions presents an opportunity for further study of unknown disease, cellular heterogeneity and aging processes.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12859-020-3443-8","type":"journal-article","created":{"date-parts":[[2020,3,17]],"date-time":"2020-03-17T03:02:29Z","timestamp":1584414149000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":115,"title":["MethylNet: an automated and modular deep learning approach for DNA methylation analysis"],"prefix":"10.1186","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8050-1291","authenticated-orcid":false,"given":"Joshua J.","family":"Levy","sequence":"first","affiliation":[]},{"given":"Alexander J.","family":"Titus","sequence":"additional","affiliation":[]},{"given":"Curtis L.","family":"Petersen","sequence":"additional","affiliation":[]},{"given":"Youdinghuan","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Lucas A.","family":"Salas","sequence":"additional","affiliation":[]},{"given":"Brock C.","family":"Christensen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,3,17]]},"reference":[{"key":"3443_CR1","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun Y, Bengio Y, Hinton G. 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