107 related articles for article (PubMed ID: 33250148)
1. Autoencoded DNA methylation data to predict breast cancer recurrence: Machine learning models and gene-weight significance.
Macías-García L; Martínez-Ballesteros M; Luna-Romera JM; García-Heredia JM; García-Gutiérrez J; Riquelme-Santos JC
Artif Intell Med; 2020 Nov; 110():101976. PubMed ID: 33250148
[TBL] [Abstract][Full Text] [Related]
2. 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]
3. Expression and methylation patterns partition luminal-A breast tumors into distinct prognostic subgroups.
Netanely D; Avraham A; Ben-Baruch A; Evron E; Shamir R
Breast Cancer Res; 2016 Jul; 18(1):74. PubMed ID: 27386846
[TBL] [Abstract][Full Text] [Related]
4.
Zhang SL; Yu HJ; Lian ZQ; Wan J; Xie SM; Lei W; Chen QP; Zhang L; Wang Q
J Int Med Res; 2024 Jan; 52(1):3000605231220827. PubMed ID: 38180895
[TBL] [Abstract][Full Text] [Related]
5. Quantitative DNA methylation and recurrence of breast cancer: a study of 30 candidate genes.
Cheol Kim D; Thorat MA; Lee MR; Cho SH; Vasiljević N; Scibior-Bentkowska D; Wu K; Ahmad AS; Duffy S; Cuzick JM; Lorincz AT
Cancer Biomark; 2012; 11(2-3):75-88. PubMed ID: 23011154
[TBL] [Abstract][Full Text] [Related]
6. A hybrid metaheuristic-deep learning technique for the pan-classification of cancer based on DNA methylation.
Eissa NS; Khairuddin U; Yusof R
BMC Bioinformatics; 2022 Jul; 23(1):273. PubMed ID: 35818034
[TBL] [Abstract][Full Text] [Related]
7. DNA methylation in breast cancers: Differences based on estrogen receptor status and recurrence.
Williams KE; Jawale RM; Schneider SS; Otis CN; Pentecost BT; Arcaro KF
J Cell Biochem; 2019 Jan; 120(1):738-755. PubMed ID: 30230580
[TBL] [Abstract][Full Text] [Related]
8. Computational Detection of Breast Cancer Invasiveness with DNA Methylation Biomarkers.
Wang C; Zhao N; Yuan L; Liu X
Cells; 2020 Jan; 9(2):. PubMed ID: 32019269
[TBL] [Abstract][Full Text] [Related]
9. 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]
10. DNA methylation and hormone receptor status in breast cancer.
Benevolenskaya EV; Islam AB; Ahsan H; Kibriya MG; Jasmine F; Wolff B; Al-Alem U; Wiley E; Kajdacsy-Balla A; Macias V; Rauscher GH
Clin Epigenetics; 2016; 8():17. PubMed ID: 26884818
[TBL] [Abstract][Full Text] [Related]
11. DNA Methylation Patterns Can Estimate Nonequivalent Outcomes of Breast Cancer with the Same Receptor Subtypes.
Zhang M; Zhang S; Wen Y; Wang Y; Wei Y; Liu H; Zhang D; Su J; Wang F; Zhang Y
PLoS One; 2015; 10(11):e0142279. PubMed ID: 26550991
[TBL] [Abstract][Full Text] [Related]
12. The method for breast cancer grade prediction and pathway analysis based on improved multiple kernel learning.
Song T; Wang Y; Du W; Cao S; Tian Y; Liang Y
J Bioinform Comput Biol; 2017 Feb; 15(1):1650037. PubMed ID: 27899048
[TBL] [Abstract][Full Text] [Related]
13. Prognostic DNA methylation markers for hormone receptor breast cancer: a systematic review.
de Ruijter TC; van der Heide F; Smits KM; Aarts MJ; van Engeland M; Heijnen VCG
Breast Cancer Res; 2020 Jan; 22(1):13. PubMed ID: 32005275
[TBL] [Abstract][Full Text] [Related]
14. Identification of epigenetic modulators in human breast cancer by integrated analysis of DNA methylation and RNA-Seq data.
Zhou X; Chen Z; Cai X
Epigenetics; 2018; 13(5):473-489. PubMed ID: 29940789
[TBL] [Abstract][Full Text] [Related]
15. Ranking genomic features using an information-theoretic measure of epigenetic discordance.
Jenkinson G; Abante J; Koldobskiy MA; Feinberg AP; Goutsias J
BMC Bioinformatics; 2019 Apr; 20(1):175. PubMed ID: 30961526
[TBL] [Abstract][Full Text] [Related]
16. Genome-wide analysis and modeling of DNA methylation susceptibility in 30 breast cancer cell lines by using CpG flanking sequences.
An J; Kim K; Rhee SM; Chae H; Nephew KP; Kim S
J Bioinform Comput Biol; 2013 Jun; 11(3):1341003. PubMed ID: 23796180
[TBL] [Abstract][Full Text] [Related]
17. Predicting censored survival data based on the interactions between meta-dimensional omics data in breast cancer.
Kim D; Li R; Dudek SM; Ritchie MD
J Biomed Inform; 2015 Aug; 56():220-8. PubMed ID: 26048077
[TBL] [Abstract][Full Text] [Related]
18. Machine learning integrated ensemble of feature selection methods followed by survival analysis for predicting breast cancer subtype specific miRNA biomarkers.
Sarkar JP; Saha I; Sarkar A; Maulik U
Comput Biol Med; 2021 Apr; 131():104244. PubMed ID: 33550016
[TBL] [Abstract][Full Text] [Related]
19. Candidate methylation sites associated with endocrine therapy resistance in ER+/HER2- breast cancer.
Soleimani Dodaran M; Borgoni S; Sofyalı E; Verschure PJ; Wiemann S; Moerland PD; van Kampen AHC
BMC Cancer; 2020 Jul; 20(1):676. PubMed ID: 32684154
[TBL] [Abstract][Full Text] [Related]
20. Unsupervised feature construction and knowledge extraction from genome-wide assays of breast cancer with denoising autoencoders.
Tan J; Ung M; Cheng C; Greene CS
Pac Symp Biocomput; 2015; 20():132-43. PubMed ID: 25592575
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]