| Rauschert, S and Melton, PE and Heiskala, A and Karhunen, V and Burdge, G and Craig, JM and Godfrey, KM and Lillycrop, K and Mori, TA and Beilin, LJ and Oddy, WH and Pennell, C and Jarvelin, M-R and Sebert, S and Huang, R-C, “Machine learning-based DNA methylation score for fetal exposure to maternal smoking: development and validation in samples collected from adolescents and adults”, Environmental Health Perspectives, 128 (9) Article 97003. ISSN 0091-6765 (2020) [Refereed Article] |
| Data Type | Value |
|---|
| Type of Research | Applied Research |
| Research Division | Biomedical and Clinical Sciences |
| Research Group | Reproductive medicine |
| Research Field | Foetal development and medicine |
| Research Objective Division | Health |
| Research Objective Group | Public health (excl. specific population health) |
| Research Objective Field | Public health (excl. specific population health) not elsewhere classified |
| Visit Item on eCite | http://ecite.utas.edu.au/141184 |
| Digital Object Identifier | doi:10.1289/EHP6076 |
| Scopus Source URL | View the full record on Scopus |
| Scopus Citing URL | View the list of citing articles on Scopus |
| Web of Science® Source URL | View the full record on Web of Science® |
| Web of Science® Citing URL | View the list of citing articles on Web of Science® |
| Web of Science® Related URL | View the list of related articles on Web of Science® |
| Number of Times Cited | 12 |
| Number of Downloads | 39 |
