200 related articles for article (PubMed ID: 28401970)
1. Integrating multiple omics data for the discovery of potential Beclin-1 interactions in breast cancer.
Chen Y; Wang X; Wang G; Li Z; Wang J; Huang L; Qin Z; Yuan X; Cheng Z; Zhang S; Yin Y; He J
Mol Biosyst; 2017 May; 13(5):991-999. PubMed ID: 28401970
[TBL] [Abstract][Full Text] [Related]
2. 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]
3. MOBCdb: a comprehensive database integrating multi-omics data on breast cancer for precision medicine.
Xie B; Yuan Z; Yang Y; Sun Z; Zhou S; Fang X
Breast Cancer Res Treat; 2018 Jun; 169(3):625-632. PubMed ID: 29429018
[TBL] [Abstract][Full Text] [Related]
4. BRCA-Pathway: a structural integration and visualization system of TCGA breast cancer data on KEGG pathways.
Kim I; Choi S; Kim S
BMC Bioinformatics; 2018 Feb; 19(Suppl 1):42. PubMed ID: 29504910
[TBL] [Abstract][Full Text] [Related]
5. Multi-omics facilitated variable selection in Cox-regression model for cancer prognosis prediction.
Liu C; Wang X; Genchev GZ; Lu H
Methods; 2017 Jul; 124():100-107. PubMed ID: 28627406
[TBL] [Abstract][Full Text] [Related]
6. MicroRNA-20a-mediated loss of autophagy contributes to breast tumorigenesis by promoting genomic damage and instability.
Liu L; He J; Wei X; Wan G; Lao Y; Xu W; Li Z; Hu H; Hu Z; Luo X; Wu J; Xie W; Zhang Y; Xu N
Oncogene; 2017 Oct; 36(42):5874-5884. PubMed ID: 28628113
[TBL] [Abstract][Full Text] [Related]
7. Integrating multi-omics data by learning modality invariant representations for improved prediction of overall survival of cancer.
Tong L; Wu H; Wang MD
Methods; 2021 May; 189():74-85. PubMed ID: 32763377
[TBL] [Abstract][Full Text] [Related]
8. Pathway Relevance Ranking for Tumor Samples through Network-Based Data Integration.
Verbeke LP; Van den Eynden J; Fierro AC; Demeester P; Fostier J; Marchal K
PLoS One; 2015; 10(7):e0133503. PubMed ID: 26217958
[TBL] [Abstract][Full Text] [Related]
9. High-throughput «Omics» technologies: New tools for the study of triple-negative breast cancer.
Judes G; Rifaï K; Daures M; Dubois L; Bignon YJ; Penault-Llorca F; Bernard-Gallon D
Cancer Lett; 2016 Nov; 382(1):77-85. PubMed ID: 26965997
[TBL] [Abstract][Full Text] [Related]
10. Identification of candidate cancer drivers by integrative Epi-DNA and Gene Expression (iEDGE) data analysis.
Li A; Chapuy B; Varelas X; Sebastiani P; Monti S
Sci Rep; 2019 Nov; 9(1):16904. PubMed ID: 31729402
[TBL] [Abstract][Full Text] [Related]
11. Identification of Novel Breast Cancer Subtype-Specific Biomarkers by Integrating Genomics Analysis of DNA Copy Number Aberrations and miRNA-mRNA Dual Expression Profiling.
Li D; Xia H; Li ZY; Hua L; Li L
Biomed Res Int; 2015; 2015():746970. PubMed ID: 25961039
[TBL] [Abstract][Full Text] [Related]
12. An Integrative Approach for Identifying Network Biomarkers of Breast Cancer Subtypes Using Genomic, Interactomic, and Transcriptomic Data.
Firoozbakht F; Rezaeian I; D'agnillo M; Porter L; Rueda L; Ngom A
J Comput Biol; 2017 Aug; 24(8):756-766. PubMed ID: 28650678
[TBL] [Abstract][Full Text] [Related]
13. Distinct patterns of DNA copy number alteration are associated with different clinicopathological features and gene-expression subtypes of breast cancer.
Bergamaschi A; Kim YH; Wang P; Sørlie T; Hernandez-Boussard T; Lonning PE; Tibshirani R; Børresen-Dale AL; Pollack JR
Genes Chromosomes Cancer; 2006 Nov; 45(11):1033-40. PubMed ID: 16897746
[TBL] [Abstract][Full Text] [Related]
14. MethCNA: a database for integrating genomic and epigenomic data in human cancer.
Deng G; Yang J; Zhang Q; Xiao ZX; Cai H
BMC Genomics; 2018 Feb; 19(1):138. PubMed ID: 29433427
[TBL] [Abstract][Full Text] [Related]
15. Classifying Breast Cancer Subtypes Using Deep Neural Networks Based on Multi-Omics Data.
Lin Y; Zhang W; Cao H; Li G; Du W
Genes (Basel); 2020 Aug; 11(8):. PubMed ID: 32759821
[TBL] [Abstract][Full Text] [Related]
16. 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]
17. Cancer driver gene discovery through an integrative genomics approach in a non-parametric Bayesian framework.
Yang H; Wei Q; Zhong X; Yang H; Li B
Bioinformatics; 2017 Feb; 33(4):483-490. PubMed ID: 27797769
[TBL] [Abstract][Full Text] [Related]
18. Integrated genomic analysis for prediction of survival for patients with liver cancer using The Cancer Genome Atlas.
Song YZ; Li X; Li W; Wang Z; Li K; Xie FL; Zhang F
World J Gastroenterol; 2018 Jul; 24(28):3145-3154. PubMed ID: 30065560
[TBL] [Abstract][Full Text] [Related]
19. 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]
20. Autophagy regulator BECN1 suppresses mammary tumorigenesis driven by WNT1 activation and following parity.
Cicchini M; Chakrabarti R; Kongara S; Price S; Nahar R; Lozy F; Zhong H; Vazquez A; Kang Y; Karantza V
Autophagy; 2014; 10(11):2036-52. PubMed ID: 25483966
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
