156 related articles for article (PubMed ID: 31121299)
1. A rectified factor network based biclustering method for detecting cancer-related coding genes and miRNAs, and their interactions.
Su L; Liu G; Wang J; Xu D
Methods; 2019 Aug; 166():22-30. PubMed ID: 31121299
[TBL] [Abstract][Full Text] [Related]
2. MicroRNA expression and gene regulation drive breast cancer progression and metastasis in PyMT mice.
Nogales-Cadenas R; Cai Y; Lin JR; Zhang Q; Zhang W; Montagna C; Zhang ZD
Breast Cancer Res; 2016 Jul; 18(1):75. PubMed ID: 27449149
[TBL] [Abstract][Full Text] [Related]
3. Reconstruction of temporal activity of microRNAs from gene expression data in breast cancer cell line.
Jayavelu ND; Bar N
BMC Genomics; 2015 Dec; 16():1077. PubMed ID: 26763900
[TBL] [Abstract][Full Text] [Related]
4. Analysis of miRNA expression profiles in breast cancer using biclustering.
Fiannaca A; La Rosa M; La Paglia L; Rizzo R; Urso A
BMC Bioinformatics; 2015; 16 Suppl 4(Suppl 4):S7. PubMed ID: 25734576
[TBL] [Abstract][Full Text] [Related]
5. A novel biclustering algorithm for the discovery of meaningful biological correlations between microRNAs and their target genes.
Pio G; Ceci M; D'Elia D; Loglisci C; Malerba D
BMC Bioinformatics; 2013; 14 Suppl 7(Suppl 7):S8. PubMed ID: 23815553
[TBL] [Abstract][Full Text] [Related]
6. Prioritizing breast cancer subtype related miRNAs using miRNA-mRNA dysregulated relationships extracted from their dual expression profiling.
Hua L; Zhou P; Li L; Liu H; Yang Z
J Theor Biol; 2013 Aug; 331():1-11. PubMed ID: 23619378
[TBL] [Abstract][Full Text] [Related]
7. A novel framework for inferring condition-specific TF and miRNA co-regulation of protein-protein interactions.
Zhang J; Le TD; Liu L; He J; Li J
Gene; 2016 Feb; 577(1):55-64. PubMed ID: 26611531
[TBL] [Abstract][Full Text] [Related]
8. Regulatory network reconstruction of five essential microRNAs for survival analysis in breast cancer by integrating miRNA and mRNA expression datasets.
He K; Li WX; Guan D; Gong M; Ye S; Fang Z; Huang JF; Lu A
Funct Integr Genomics; 2019 Jul; 19(4):645-658. PubMed ID: 30859354
[TBL] [Abstract][Full Text] [Related]
9. Identifying cancer-related microRNAs based on gene expression data.
Zhao XM; Liu KQ; Zhu G; He F; Duval B; Richer JM; Huang DS; Jiang CJ; Hao JK; Chen L
Bioinformatics; 2015 Apr; 31(8):1226-34. PubMed ID: 25505085
[TBL] [Abstract][Full Text] [Related]
10. Biclustering analysis of transcriptome big data identifies condition-specific microRNA targets.
Yoon S; Nguyen HCT; Jo W; Kim J; Chi SM; Park J; Kim SY; Nam D
Nucleic Acids Res; 2019 May; 47(9):e53. PubMed ID: 30820547
[TBL] [Abstract][Full Text] [Related]
11. Identifying miRNA sponge modules using biclustering and regulatory scores.
Zhang J; Le TD; Liu L; Li J
BMC Bioinformatics; 2017 Mar; 18(Suppl 3):44. PubMed ID: 28361682
[TBL] [Abstract][Full Text] [Related]
12. An integrated network of microRNA and gene expression in ovarian cancer.
Quitadamo A; Tian L; Hall B; Shi X
BMC Bioinformatics; 2015; 16 Suppl 5(Suppl 5):S5. PubMed ID: 25860109
[TBL] [Abstract][Full Text] [Related]
13. A Novel Method to Detect Functional microRNA Regulatory Modules by Bicliques Merging.
Liang C; Li Y; Luo J
IEEE/ACM Trans Comput Biol Bioinform; 2016; 13(3):549-56. PubMed ID: 27295638
[TBL] [Abstract][Full Text] [Related]
14. Inferred miRNA activity identifies miRNA-mediated regulatory networks underlying multiple cancers.
Lee E; Ito K; Zhao Y; Schadt EE; Irie HY; Zhu J
Bioinformatics; 2016 Jan; 32(1):96-105. PubMed ID: 26358730
[TBL] [Abstract][Full Text] [Related]
15. Crucial microRNAs and genes of human primary breast cancer explored by microRNA-mRNA integrated analysis.
Yang Y; Xing Y; Liang C; Hu L; Xu F; Chen Y
Tumour Biol; 2015 Jul; 36(7):5571-9. PubMed ID: 25680412
[TBL] [Abstract][Full Text] [Related]
16. Identification of potential miRNA-mRNA regulatory network contributing to pathogenesis of HBV-related HCC.
Lou W; Liu J; Ding B; Chen D; Xu L; Ding J; Jiang D; Zhou L; Zheng S; Fan W
J Transl Med; 2019 Jan; 17(1):7. PubMed ID: 30602391
[TBL] [Abstract][Full Text] [Related]
17. Competing endogenous RNA network analysis identifies critical genes among the different breast cancer subtypes.
Chen J; Xu J; Li Y; Zhang J; Chen H; Lu J; Wang Z; Zhao X; Xu K; Li Y; Li X; Zhang Y
Oncotarget; 2017 Feb; 8(6):10171-10184. PubMed ID: 28052038
[TBL] [Abstract][Full Text] [Related]
18. Identifying condition specific key genes from basal-like breast cancer gene expression data.
Maind A; Raut S
Comput Biol Chem; 2019 Feb; 78():367-374. PubMed ID: 30655072
[TBL] [Abstract][Full Text] [Related]
19. Identifying Cancer Subtypes from miRNA-TF-mRNA Regulatory Networks and Expression Data.
Xu T; Le TD; Liu L; Wang R; Sun B; Li J
PLoS One; 2016; 11(4):e0152792. PubMed ID: 27035433
[TBL] [Abstract][Full Text] [Related]
20. Identifying miRNA-mRNA regulatory relationships in breast cancer with invariant causal prediction.
Pham VV; Zhang J; Liu L; Truong B; Xu T; Nguyen TT; Li J; Le TD
BMC Bioinformatics; 2019 Mar; 20(1):143. PubMed ID: 30876399
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]