106 related articles for article (PubMed ID: 30484698)
1. An accurate regression of developmental stages for breast cancer based on transcriptomic biomarkers.
Feng X; Zhang R; Liu M; Liu Q; Li F; Yan Z; Zhou F
Biomark Med; 2019 Jan; 13(1):5-15. PubMed ID: 30484698
[TBL] [Abstract][Full Text] [Related]
2. Ridle for sparse regression with mandatory covariates with application to the genetic assessment of histologic grades of breast cancer.
Zhai J; Hsu CH; Daye ZJ
BMC Med Res Methodol; 2017 Jan; 17(1):12. PubMed ID: 28122498
[TBL] [Abstract][Full Text] [Related]
3. Identification of blood protein biomarkers for breast cancer staging by integrative transcriptome and proteome analyses.
Yao F; Yan C; Zhang Y; Shen L; Zhou D; Ni J
J Proteomics; 2021 Jan; 230():103991. PubMed ID: 32971305
[TBL] [Abstract][Full Text] [Related]
4. Empirical extensions of the lasso penalty to reduce the false discovery rate in high-dimensional Cox regression models.
Ternès N; Rotolo F; Michiels S
Stat Med; 2016 Jul; 35(15):2561-73. PubMed ID: 26970107
[TBL] [Abstract][Full Text] [Related]
5. In silico markers: an evolutionary and statistical approach to select informative genes of human breast cancer subtypes.
Bhowmick SS; Bhattacharjee D; Rato L
Genes Genomics; 2019 Dec; 41(12):1371-1382. PubMed ID: 31004329
[TBL] [Abstract][Full Text] [Related]
6. Comprehensive tissue-specific gene set enrichment analysis and transcription factor analysis of breast cancer by integrating 14 gene expression datasets.
Li WX; He K; Tang L; Dai SX; Li GH; Lv WW; Guo YC; An SQ; Wu GY; Liu D; Huang JF
Oncotarget; 2017 Jan; 8(4):6775-6786. PubMed ID: 28036274
[TBL] [Abstract][Full Text] [Related]
7. Identification of Gene-Expression Signatures and Protein Markers for Breast Cancer Grading and Staging.
Yao F; Zhang C; Du W; Liu C; Xu Y
PLoS One; 2015; 10(9):e0138213. PubMed ID: 26375396
[TBL] [Abstract][Full Text] [Related]
8. 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]
9. A Comparison of Two-Stage Approaches Based on Penalized Regression for Estimating Gene Networks.
Lee M; Seok J; Tae D; Zhong H; Han SW
J Comput Biol; 2017 Jul; 24(7):709-720. PubMed ID: 28541712
[TBL] [Abstract][Full Text] [Related]
10. Biclustering reveals breast cancer tumour subgroups with common clinical features and improves prediction of disease recurrence.
Wang YK; Print CG; Crampin EJ
BMC Genomics; 2013 Feb; 14():102. PubMed ID: 23405961
[TBL] [Abstract][Full Text] [Related]
11. An ensemble predictive modeling framework for breast cancer classification.
Nagarajan R; Upreti M
Methods; 2017 Dec; 131():128-134. PubMed ID: 28716511
[TBL] [Abstract][Full Text] [Related]
12. BioDog, biomarker detection for improving identification power of breast cancer histologic grade in methylomics.
Zhang Y; Chen C; Duan M; Liu S; Huang L; Zhou F
Epigenomics; 2019 Nov; 11(15):1717-1732. PubMed ID: 31625763
[No Abstract] [Full Text] [Related]
13. Is the TNM staging system for breast cancer still relevant in the era of biomarkers and emerging personalized medicine for breast cancer - an institution's 10-year experience.
Orucevic A; Chen J; McLoughlin JM; Heidel RE; Panella T; Bell J
Breast J; 2015; 21(2):147-54. PubMed ID: 25600504
[TBL] [Abstract][Full Text] [Related]
14. Molecular signatures in breast cancer.
Lal S; McCart Reed AE; de Luca XM; Simpson PT
Methods; 2017 Dec; 131():135-146. PubMed ID: 28669865
[TBL] [Abstract][Full Text] [Related]
15. Computational selection of antibody-drug conjugate targets for breast cancer.
Fauteux F; Hill JJ; Jaramillo ML; Pan Y; Phan S; Famili F; O'Connor-McCourt M
Oncotarget; 2016 Jan; 7(3):2555-71. PubMed ID: 26700623
[TBL] [Abstract][Full Text] [Related]
16. 21-Gene recurrence score and locoregional recurrence in lymph node-negative, estrogen receptor-positive breast cancer.
Turashvili G; Chou JF; Brogi E; Morrow M; Dickler M; Norton L; Hudis C; Wen HY
Breast Cancer Res Treat; 2017 Nov; 166(1):69-76. PubMed ID: 28702894
[TBL] [Abstract][Full Text] [Related]
17. Integration of somatic mutation, expression and functional data reveals potential driver genes predictive of breast cancer survival.
Suo C; Hrydziuszko O; Lee D; Pramana S; Saputra D; Joshi H; Calza S; Pawitan Y
Bioinformatics; 2015 Aug; 31(16):2607-13. PubMed ID: 25810432
[TBL] [Abstract][Full Text] [Related]
18. Multigene assays: Implications for breast cancer staging.
Greene FL
J Surg Oncol; 2017 May; 115(6):663-664. PubMed ID: 28493552
[No Abstract] [Full Text] [Related]
19. Investigating the therapeutic potential and mechanism of curcumin in breast cancer based on RNA sequencing and bioinformatics analysis.
Wang R; Li J; Zhao Y; Li Y; Yin L
Breast Cancer; 2018 Mar; 25(2):206-212. PubMed ID: 29139094
[TBL] [Abstract][Full Text] [Related]
20. A favorable role of prolactin in human breast cancer reveals novel pathway-based gene signatures indicative of tumor differentiation and favorable patient outcome.
Hachim IY; Shams A; Lebrun JJ; Ali S
Hum Pathol; 2016 Jul; 53():142-52. PubMed ID: 26980025
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
