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Investigating the DNA methylation profile of e-cigarette use.

Clinical epigenetics

Richmond RC, Sillero-Rejon C, Khouja JN, Prince C, Board A, Sharp G, Suderman M, Relton CL, Munafò M, Gage SH.
PMID: 34583751
Clin Epigenetics. 2021 Sep 28;13(1):183. doi: 10.1186/s13148-021-01174-7.

BACKGROUND: Little evidence exists on the health effects of e-cigarette use. DNA methylation may serve as a biomarker for exposure and could be predictive of future health risk. We aimed to investigate the DNA methylation profile of e-cigarette use.RESULTS:...

Cite
Richmond RC, Sillero-Rejon C, Khouja JN, et al. Investigating the DNA methylation profile of e-cigarette use. Clin Epigenetics. 2021;13(1):183doi: 10.1186/s13148-021-01174-7.
Richmond, R. C., Sillero-Rejon, C., Khouja, J. N., Prince, C., Board, A., Sharp, G., Suderman, M., Relton, C. L., Munafò, M., & Gage, S. H. (2021). Investigating the DNA methylation profile of e-cigarette use. Clinical epigenetics, 13(1), 183. https://doi.org/10.1186/s13148-021-01174-7
Richmond, Rebecca C, et al. "Investigating the DNA methylation profile of e-cigarette use." Clinical epigenetics vol. 13,1 (2021): 183. doi: https://doi.org/10.1186/s13148-021-01174-7
Richmond RC, Sillero-Rejon C, Khouja JN, Prince C, Board A, Sharp G, Suderman M, Relton CL, Munafò M, Gage SH. Investigating the DNA methylation profile of e-cigarette use. Clin Epigenetics. 2021 Sep 28;13(1):183. doi: 10.1186/s13148-021-01174-7. PMID: 34583751; PMCID: PMC8479883.
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Investigating the DNA methylation profile of e-cigarette use.

Clinical epigenetics

Richmond RC, Sillero-Rejon C, Khouja JN, Prince C, Board A, Sharp G, Suderman M, Relton CL, Munafò M, Gage SH.
PMID: 34583751
Clin Epigenetics. 2021 Sep 28;13(1):183. doi: 10.1186/s13148-021-01174-7.

BACKGROUND: Little evidence exists on the health effects of e-cigarette use. DNA methylation may serve as a biomarker for exposure and could be predictive of future health risk. We aimed to investigate the DNA methylation profile of e-cigarette use.RESULTS:...

Cite
Richmond RC, Sillero-Rejon C, Khouja JN, et al. Investigating the DNA methylation profile of e-cigarette use. Clin Epigenetics. 2021;13(1):183doi: 10.1186/s13148-021-01174-7.
Richmond, R. C., Sillero-Rejon, C., Khouja, J. N., Prince, C., Board, A., Sharp, G., Suderman, M., Relton, C. L., Munafò, M., & Gage, S. H. (2021). Investigating the DNA methylation profile of e-cigarette use. Clinical epigenetics, 13(1), 183. https://doi.org/10.1186/s13148-021-01174-7
Richmond, Rebecca C, et al. "Investigating the DNA methylation profile of e-cigarette use." Clinical epigenetics vol. 13,1 (2021): 183. doi: https://doi.org/10.1186/s13148-021-01174-7
Richmond RC, Sillero-Rejon C, Khouja JN, Prince C, Board A, Sharp G, Suderman M, Relton CL, Munafò M, Gage SH. Investigating the DNA methylation profile of e-cigarette use. Clin Epigenetics. 2021 Sep 28;13(1):183. doi: 10.1186/s13148-021-01174-7. PMID: 34583751; PMCID: PMC8479883.
Copy Download .nbib Format: NLM
Prediction of driver variants in the cancer genome via machine learning methodologies.

Briefings in bioinformatics

Rogers MF, Gaunt TR, Campbell C.
PMID: 33094325
Brief Bioinform. 2021 Jul 20;22(4). doi: 10.1093/bib/bbaa250.

Sequencing technologies have led to the identification of many variants in the human genome which could act as disease-drivers. As a consequence, a variety of bioinformatics tools have been proposed for predicting which variants may drive disease, and which...

Cite
Rogers MF, Gaunt TR, Campbell C. Prediction of driver variants in the cancer genome via machine learning methodologies. Brief Bioinform. 2021;22(4)doi: 10.1093/bib/bbaa250.
Rogers, M. F., Gaunt, T. R., & Campbell, C. (2021). Prediction of driver variants in the cancer genome via machine learning methodologies. Briefings in bioinformatics, 22(4), . https://doi.org/10.1093/bib/bbaa250
Rogers, Mark F, et al. "Prediction of driver variants in the cancer genome via machine learning methodologies." Briefings in bioinformatics vol. 22,4 (2021). doi: https://doi.org/10.1093/bib/bbaa250
Rogers MF, Gaunt TR, Campbell C. Prediction of driver variants in the cancer genome via machine learning methodologies. Brief Bioinform. 2021 Jul 20;22(4). doi: 10.1093/bib/bbaa250. PMID: 33094325; PMCID: PMC8293831.
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