{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T14:43:50Z","timestamp":1786977830184,"version":"build-2736575974"},"reference-count":29,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2023,2,19]],"date-time":"2023-02-19T00:00:00Z","timestamp":1676764800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Genetic Epidemiology"],"published-print":{"date-parts":[[2023,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Gene\u2013environment (G\u2013E) interaction analysis plays an important role in studying complex diseases. Extensive methodological research has been conducted on G\u2013E interaction analysis, and the existing methods are mostly based on regression techniques. In many fields including biomedicine and omics, it has been increasingly recognized that deep learning may outperform regression with its unique flexibility (e.g., in accommodating unspecified nonlinear effects) and superior prediction performance. However, there has been a lack of development in deep learning for G\u2013E interaction analysis. In this article, we fill this important knowledge gap and develop a new analysis approach based on deep neural network in conjunction with penalization. The proposed approach can simultaneously conduct model estimation and selection (of important main G effects and G\u2013E interactions), while uniquely respecting the \u201cmain effects, interactions\u201d variable selection hierarchy. Simulation shows that it has superior prediction and feature selection performance. The analysis of data on lung adenocarcinoma\u00a0and skin cutaneous melanoma\u00a0overall survival further establishes its practical utility. Overall, this study can advance G\u2013E interaction analysis by delivering a powerful new analysis approach based on modern deep learning.<\/jats:p>","DOI":"10.1002\/gepi.22518","type":"journal-article","created":{"date-parts":[[2023,2,19]],"date-time":"2023-02-19T17:37:15Z","timestamp":1676828235000},"page":"261-286","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Gene\u2013environment interaction analysis via deep learning"],"prefix":"10.1002","volume":"47","author":[{"given":"Shuni","family":"Wu","sequence":"first","affiliation":[{"name":"The Wang Yanan Institute for Studies in Economics Xiamen University Xiamen China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yaqing","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Epidemiology and Biostatistics, School of Public Health Shanghai Jiao Tong University School of Medicine Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingzhao","family":"Zhang","sequence":"additional","affiliation":[{"name":"The Wang Yanan Institute for Studies in Economics Xiamen University Xiamen China"},{"name":"Department of Statistics and Data Science, School of Economics and Fujian Key Lab of Statistics Xiamen University Xiamen China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9001-4999","authenticated-orcid":false,"given":"Shuangge","family":"Ma","sequence":"additional","affiliation":[{"name":"Department of Biostatistics Yale School of Public Health New Haven Connecticut USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2023,2,19]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1214\/13-AOS1096"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2011.105"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1006076"},{"key":"e_1_2_9_5_1","volume-title":"Analysis of survival data","author":"Cox D.","year":"1984"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1198\/016214501753382273"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1038\/nrg1578"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbab454"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12874-018-0482-1"},{"key":"e_1_2_9_10_1","unstructured":"Kuka\u010dka J. Golkov V. &Cremers D.(2017). Regularization for deep learning: A taxonomy.arXiv[Preprint].https:\/\/doi.org\/10.48550\/arXiv.1710.10686"},{"key":"e_1_2_9_11_1","unstructured":"Kvamme H. Borgan \u00d8. &Scheel I.(2019). Time\u2010to\u2010event prediction with neural networks and cox regression.arXiv[Preprint].https:\/\/doi.org\/10.48550\/arXiv.1907.00825"},{"key":"e_1_2_9_12_1","doi-asserted-by":"crossref","unstructured":"Lee C. Zame W. Yoon J. &Van Der Schaar M.(2018). Deephit: A deep learning approach to survival analysis with competing risks. InS.Mcllraith &K.Weinberger(Eds.) 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