{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"bioRxiv"}],"indexed":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T04:08:32Z","timestamp":1789013312221,"version":"build-2803163510"},"posted":{"date-parts":[[2026,9,7]]},"group-title":"Bioinformatics","reference-count":17,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T00:00:00Z","timestamp":1788739200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Eunice Kennedy Shriver National Institute of Child Health and Human Development","award":["P01 HD106414"],"award-info":[{"award-number":["P01 HD106414"]}]},{"name":"Eunice Kennedy Shriver National Institute of Child Health and Human Development","award":["R01 HD089511"],"award-info":[{"award-number":["R01 HD089511"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2026,9,7]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:p>Endometriosis is a chronic inflammatory disease associated with pelvic pain, infertility, and delayed diagnosis. Growing evidence suggests that altered DNA methylation contributes to disease development and could serve as a biomarker for disease. We developed a leakage-safe machine learning pipeline to classify endometriosis case-control status and menstrual cycle phase using genome-wide DNA methylation data from eutopic endometrial tissue. The dataset consisted of 984 samples profiled using the Illumina Infinium MethylationEPIC array, with measurements across approximately 759,000 CpG sites. Technical variation was corrected using SmartSVA batch correction. Ridge logistic regression models were trained using stratified 80\/20 train-test splits, with regularization strength selected via stratified cross-validation. Feature selection approaches included ridge coefficient ranking, per-CpG t-tests, and univariate logistic regression with FDR correction. Model validity was evaluated using label-shuffling analyses. Menstrual cycle phase classification showed strong performance (mean cross-validation AUROC: 0.971, held-out test AUROC: 0.989), reflecting genome-wide hormonally driven methylation. Ridge regression produced lower but meaningful performance for endometriosis classification (mean cross-validation AUROC: 0.854, held-out test AUROC: 0.875). Ridge coefficient-based feature selection identified compact predictive CpG sets, supporting the hypothesis that endometriosis-associated methylation signal is distributed across many loci rather than a few highly predictive CpGs. Pathway enrichment analyses identified substantial enrichment for menstrual cycle phase but limited enrichment for disease status following FDR correction, consistent with a diffuse endometriosis-associated signal. These findings demonstrate that ridge regression can detect methylation patterns associated with both endometriosis and menstrual cycle phase, highlighting the importance of accounting for cycle-related epigenetic variation in endometrial DNA methylation studies.<\/jats:p>\n                <jats:sec id=\"s1\">\n                  <jats:title>Graphical Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:fig id=\"ufig1\" position=\"float\" orientation=\"portrait\" fig-type=\"figure\">\n                      <jats:graphic xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"749016v1_ufig1\" position=\"float\" orientation=\"portrait\"\/>\n                    <\/jats:fig>\n                  <\/jats:p>\n                <\/jats:sec>","DOI":"10.64898\/2026.09.02.749016","type":"posted-content","created":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T11:20:20Z","timestamp":1788780020000},"source":"Crossref","is-referenced-by-count":0,"title":["Predicting Endometriosis Status and Menstrual Cycle Phase Using DNA Methylation"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-1211-4830","authenticated-orcid":false,"given":"Amrita","family":"Nagasuri","sequence":"first","affiliation":[{"name":"Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Umair","family":"Khan","sequence":"additional","affiliation":[{"name":"Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2024-119X","authenticated-orcid":false,"given":"Parker","family":"Grosjean","sequence":"additional","affiliation":[{"name":"Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adi","family":"Siddharth","sequence":"additional","affiliation":[{"name":"Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Idit","family":"Kosti","sequence":"additional","affiliation":[{"name":"Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2327-9465","authenticated-orcid":false,"given":"Sally","family":"Mortlock","sequence":"additional","affiliation":[{"name":"The Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia"},{"name":"Australian Women and Girls\u2019 Health Research Centre, School of Public Health, The University of Queensland, Brisbane, QLD, 4006, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3163-5298","authenticated-orcid":false,"given":"Sahar","family":"Houshdaran","sequence":"additional","affiliation":[{"name":"Department of Obstetrics, Gynecology and Reproductive Sciences, University of California, San Francisco, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5169-8571","authenticated-orcid":false,"given":"Nilufer","family":"Rahmioglu","sequence":"additional","affiliation":[{"name":"Centre for Human Genetics, University of Oxford, Oxford, UK"},{"name":"Oxford Endometriosis CaRe Centre, Nuffield Department of Women\u2019s and Reproductive Health, John Radcliffe Hospital, University of Oxford, Oxford, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3147-6768","authenticated-orcid":false,"given":"Stacey A.","family":"Missmer","sequence":"additional","affiliation":[{"name":"Department of Epidemiology, Harvard T.H. 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