190 related articles for article (PubMed ID: 32649756)
1. Imputing missing RNA-sequencing data from DNA methylation by using a transfer learning-based neural network.
Zhou X; Chai H; Zhao H; Luo CH; Yang Y
Gigascience; 2020 Jul; 9(7):. PubMed ID: 32649756
[TBL] [Abstract][Full Text] [Related]
2. 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]
3. Genome-wide DNA methylome variation in two genetically distinct chicken lines using MethylC-seq.
Li J; Li R; Wang Y; Hu X; Zhao Y; Li L; Feng C; Gu X; Liang F; Lamont SJ; Hu S; Zhou H; Li N
BMC Genomics; 2015 Oct; 16():851. PubMed ID: 26497311
[TBL] [Abstract][Full Text] [Related]
4. Comprehensive Whole DNA Methylome Analysis by Integrating MeDIP-seq and MRE-seq.
Xing X; Zhang B; Li D; Wang T
Methods Mol Biol; 2018; 1708():209-246. PubMed ID: 29224147
[TBL] [Abstract][Full Text] [Related]
5. Methylation-level inferences and detection of differential methylation with MeDIP-seq data.
Zhou Y; Zhu J; Zhao M; Zhang B; Jiang C; Yang X
PLoS One; 2018; 13(8):e0201586. PubMed ID: 30086146
[TBL] [Abstract][Full Text] [Related]
6. Adult porcine genome-wide DNA methylation patterns support pigs as a biomedical model.
Schachtschneider KM; Madsen O; Park C; Rund LA; Groenen MA; Schook LB
BMC Genomics; 2015 Oct; 16():743. PubMed ID: 26438392
[TBL] [Abstract][Full Text] [Related]
7. Identification of cell type-specific methylation signals in bulk whole genome bisulfite sequencing data.
Scott CA; Duryea JD; MacKay H; Baker MS; Laritsky E; Gunasekara CJ; Coarfa C; Waterland RA
Genome Biol; 2020 Jul; 21(1):156. PubMed ID: 32605651
[TBL] [Abstract][Full Text] [Related]
8. DIRECTION: a machine learning framework for predicting and characterizing DNA methylation and hydroxymethylation in mammalian genomes.
Pavlovic M; Ray P; Pavlovic K; Kotamarti A; Chen M; Zhang MQ
Bioinformatics; 2017 Oct; 33(19):2986-2994. PubMed ID: 28505334
[TBL] [Abstract][Full Text] [Related]
9. A Linear Regression and Deep Learning Approach for Detecting Reliable Genetic Alterations in Cancer Using DNA Methylation and Gene Expression Data.
Mallik S; Seth S; Bhadra T; Zhao Z
Genes (Basel); 2020 Aug; 11(8):. PubMed ID: 32806782
[TBL] [Abstract][Full Text] [Related]
10. MBDDiff: an R package designed specifically for processing MBDcap-seq datasets.
Liu Y; Wilson D; Leach RJ; Chen Y
BMC Genomics; 2016 Aug; 17 Suppl 4(Suppl 4):432. PubMed ID: 27556923
[TBL] [Abstract][Full Text] [Related]
11. Transfer learning with convolutional neural networks for cancer survival prediction using gene-expression data.
López-García G; Jerez JM; Franco L; Veredas FJ
PLoS One; 2020; 15(3):e0230536. PubMed ID: 32214348
[TBL] [Abstract][Full Text] [Related]
12. MIRA-seq for DNA methylation analysis of CpG islands.
Jung M; Kadam S; Xiong W; Rauch TA; Jin SG; Pfeifer GP
Epigenomics; 2015 Aug; 7(5):695-706. PubMed ID: 25881900
[TBL] [Abstract][Full Text] [Related]
13. Multi-network approach to identify differentially methylated gene communities in cancer.
R V; Nazeer KAA
Gene; 2019 May; 697():227-237. PubMed ID: 30797996
[TBL] [Abstract][Full Text] [Related]
14. Systematic identification and annotation of human methylation marks based on bisulfite sequencing methylomes reveals distinct roles of cell type-specific hypomethylation in the regulation of cell identity genes.
Liu H; Liu X; Zhang S; Lv J; Li S; Shang S; Jia S; Wei Y; Wang F; Su J; Wu Q; Zhang Y
Nucleic Acids Res; 2016 Jan; 44(1):75-94. PubMed ID: 26635396
[TBL] [Abstract][Full Text] [Related]
15. Classification of early and late stage liver hepatocellular carcinoma patients from their genomics and epigenomics profiles.
Kaur H; Bhalla S; Raghava GPS
PLoS One; 2019; 14(9):e0221476. PubMed ID: 31490960
[TBL] [Abstract][Full Text] [Related]
16. A pan-cancer analysis of driver gene mutations, DNA methylation and gene expressions reveals that chromatin remodeling is a major mechanism inducing global changes in cancer epigenomes.
Youn A; Kim KI; Rabadan R; Tycko B; Shen Y; Wang S
BMC Med Genomics; 2018 Nov; 11(1):98. PubMed ID: 30400878
[TBL] [Abstract][Full Text] [Related]
17. Reduced representation bisulfite sequencing to identify global alteration of DNA methylation.
Nagarajan A; Roden C; Wajapeyee N
Methods Mol Biol; 2014; 1176():23-31. PubMed ID: 25030916
[TBL] [Abstract][Full Text] [Related]
18. Pan-cancer analysis of differential DNA methylation patterns.
Shi M; Tsui SK; Wu H; Wei Y
BMC Med Genomics; 2020 Oct; 13(Suppl 10):154. PubMed ID: 33087120
[TBL] [Abstract][Full Text] [Related]
19. Combining MeDIP-seq and MRE-seq to investigate genome-wide CpG methylation.
Li D; Zhang B; Xing X; Wang T
Methods; 2015 Jan; 72():29-40. PubMed ID: 25448294
[TBL] [Abstract][Full Text] [Related]
20. Tools and Strategies for Analysis of Genome-Wide and Gene-Specific DNA Methylation Patterns.
Chatterjee A; Rodger EJ; Morison IM; Eccles MR; Stockwell PA
Methods Mol Biol; 2017; 1537():249-277. PubMed ID: 27924599
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]