120 related articles for article (PubMed ID: 26872146)
1. A Feature Selection Algorithm to Compute Gene Centric Methylation from Probe Level Methylation Data.
Baur B; Bozdag S
PLoS One; 2016; 11(2):e0148977. PubMed ID: 26872146
[TBL] [Abstract][Full Text] [Related]
2. Multiple network algorithm for epigenetic modules via the integration of genome-wide DNA methylation and gene expression data.
Ma X; Liu Z; Zhang Z; Huang X; Tang W
BMC Bioinformatics; 2017 Jan; 18(1):72. PubMed ID: 28137264
[TBL] [Abstract][Full Text] [Related]
3. A comparison of feature selection and classification methods in DNA methylation studies using the Illumina Infinium platform.
Zhuang J; Widschwendter M; Teschendorff AE
BMC Bioinformatics; 2012 Apr; 13():59. PubMed ID: 22524302
[TBL] [Abstract][Full Text] [Related]
4. Application of Feature Selection and Deep Learning for Cancer Prediction Using DNA Methylation Markers.
Gomes R; Paul N; He N; Huber AF; Jansen RJ
Genes (Basel); 2022 Aug; 13(9):. PubMed ID: 36140725
[TBL] [Abstract][Full Text] [Related]
5. Integrated data analysis reveals potential drivers and pathways disrupted by DNA methylation in papillary thyroid carcinomas.
Beltrami CM; Dos Reis MB; Barros-Filho MC; Marchi FA; Kuasne H; Pinto CAL; Ambatipudi S; Herceg Z; Kowalski LP; Rogatto SR
Clin Epigenetics; 2017; 9():45. PubMed ID: 28469731
[TBL] [Abstract][Full Text] [Related]
6. An Efficient Feature Selection Strategy Based on Multiple Support Vector Machine Technology with Gene Expression Data.
Zhang Y; Deng Q; Liang W; Zou X
Biomed Res Int; 2018; 2018():7538204. PubMed ID: 30228989
[TBL] [Abstract][Full Text] [Related]
7. Evaluation of the Infinium Methylation 450K technology.
Dedeurwaerder S; Defrance M; Calonne E; Denis H; Sotiriou C; Fuks F
Epigenomics; 2011 Dec; 3(6):771-84. PubMed ID: 22126295
[TBL] [Abstract][Full Text] [Related]
8. Using Illumina Infinium HumanMethylation 450K BeadChip to explore genome‑wide DNA methylation profiles in a human hepatocellular carcinoma cell line.
Sun N; Zhang J; Zhang C; Shi Y; Zhao B; Jiao A; Chen B
Mol Med Rep; 2018 Nov; 18(5):4446-4456. PubMed ID: 30221710
[TBL] [Abstract][Full Text] [Related]
9. Genome-wide profiling in melatonin-exposed human breast cancer cell lines identifies differentially methylated genes involved in the anticancer effect of melatonin.
Lee SE; Kim SJ; Yoon HJ; Yu SY; Yang H; Jeong SI; Hwang SY; Park CS; Park YS
J Pineal Res; 2013 Jan; 54(1):80-8. PubMed ID: 22856590
[TBL] [Abstract][Full Text] [Related]
10. The impact of DNA methylation on the cancer proteome.
Magzoub MM; Prunello M; Brennan K; Gevaert O
PLoS Comput Biol; 2019 Jul; 15(7):e1007245. PubMed ID: 31356589
[TBL] [Abstract][Full Text] [Related]
11. A systems-level integrative framework for genome-wide DNA methylation and gene expression data identifies differential gene expression modules under epigenetic control.
Jiao Y; Widschwendter M; Teschendorff AE
Bioinformatics; 2014 Aug; 30(16):2360-6. PubMed ID: 24794928
[TBL] [Abstract][Full Text] [Related]
12. A variational Bayes beta mixture model for feature selection in DNA methylation studies.
Ma Z; Teschendorff AE
J Bioinform Comput Biol; 2013 Aug; 11(4):1350005. PubMed ID: 23859269
[TBL] [Abstract][Full Text] [Related]
13. Epigenetic inactivation of the HOXA gene cluster in breast cancer.
Novak P; Jensen T; Oshiro MM; Wozniak RJ; Nouzova M; Watts GS; Klimecki WT; Kim C; Futscher BW
Cancer Res; 2006 Nov; 66(22):10664-70. PubMed ID: 17090521
[TBL] [Abstract][Full Text] [Related]
14. Epigenetic control of phospholipase A2 receptor expression in mammary cancer cells.
Menschikowski M; Hagelgans A; Nacke B; Jandeck C; Sukocheva O; Siegert G
BMC Cancer; 2015 Dec; 15():971. PubMed ID: 26672991
[TBL] [Abstract][Full Text] [Related]
15. Physical activity and breast cancer survival: an epigenetic link through reduced methylation of a tumor suppressor gene L3MBTL1.
Zeng H; Irwin ML; Lu L; Risch H; Mayne S; Mu L; Deng Q; Scarampi L; Mitidieri M; Katsaros D; Yu H
Breast Cancer Res Treat; 2012 May; 133(1):127-35. PubMed ID: 21837478
[TBL] [Abstract][Full Text] [Related]
16. Determining the effect of DNA methylation on gene expression in cancer cells.
Lee CJ; Evans J; Kim K; Chae H; Kim S
Methods Mol Biol; 2014; 1101():161-78. PubMed ID: 24233782
[TBL] [Abstract][Full Text] [Related]
17. Functional normalization of 450k methylation array data improves replication in large cancer studies.
Fortin JP; Labbe A; Lemire M; Zanke BW; Hudson TJ; Fertig EJ; Greenwood CM; Hansen KD
Genome Biol; 2014 Dec; 15(12):503. PubMed ID: 25599564
[TBL] [Abstract][Full Text] [Related]
18. Using epigenomics data to predict gene expression in lung cancer.
Li J; Ching T; Huang S; Garmire LX
BMC Bioinformatics; 2015; 16 Suppl 5(Suppl 5):S10. PubMed ID: 25861082
[TBL] [Abstract][Full Text] [Related]
19. A canonical correlation analysis-based dynamic bayesian network prior to infer gene regulatory networks from multiple types of biological data.
Baur B; Bozdag S
J Comput Biol; 2015 Apr; 22(4):289-99. PubMed ID: 25844668
[TBL] [Abstract][Full Text] [Related]
20. Exploring breast carcinogenesis through integrative genomics and epigenomics analyses.
Minning C; Mokhtar NM; Abdullah N; Muhammad R; Emran NA; Ali SA; Harun R; Jamal R
Int J Oncol; 2014 Nov; 45(5):1959-68. PubMed ID: 25175708
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]