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Gene-environment interactions: how many false positives?

, , and . Journal of the National Cancer Institute, 97 (8): 550-1 (April 2005)4144<m:linebreak></m:linebreak>LR: 20061115; PUBM: Print; JID: 7503089; CON: J Natl Cancer Inst. 2004 Mar 17;96(6):434-42. PMID: 15026468; CON: J Natl Cancer Inst. 2005 Apr 20;97(8):567-76. PMID: 15840879; ppublish;<m:linebreak></m:linebreak>Epidemiologia genètica.
DOI: 10.1093/jnci/dji122

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Improving the prediction of cardiovascular risk with machine-learning and DNA methylation data., , , , , , , , , and . CIBCB, page 1-4. IEEE, (2019)An Overview of the Genetic Structure within the Italian Population from Genome-Wide Data, , , , , , , , , and 8 other author(s). PLoS ONE, 7 (9): e43759 (September 2012)Gene-environment interactions: how many false positives?, , and . Journal of the National Cancer Institute, 97 (8): 550-1 (April 2005)4144<m:linebreak></m:linebreak>LR: 20061115; PUBM: Print; JID: 7503089; CON: J Natl Cancer Inst. 2004 Mar 17;96(6):434-42. PMID: 15026468; CON: J Natl Cancer Inst. 2005 Apr 20;97(8):567-76. PMID: 15840879; ppublish;<m:linebreak></m:linebreak>Epidemiologia genètica.TIGER: technical variation elimination for metabolomics data using ensemble learning architecture., , , , , , , , , and 2 other author(s). Briefings Bioinform., (2022)