99 related articles for article (PubMed ID: 29516986)
1. Combining differential expression and differential coexpression analysis identifies optimal gene and gene set in cervical cancer.
Fang SQ; Gao M; Xiong SL; Chen HY; Hu SS; Cai HB
J Cancer Res Ther; 2018 Jan; 14(1):201-207. PubMed ID: 29516986
[TBL] [Abstract][Full Text] [Related]
2. Identifying the optimal gene and gene set in hepatocellular carcinoma based on differential expression and differential co-expression algorithm.
Dong LY; Zhou WZ; Ni JW; Xiang W; Hu WH; Yu C; Li HY
Oncol Rep; 2017 Feb; 37(2):1066-1074. PubMed ID: 28035405
[TBL] [Abstract][Full Text] [Related]
3. Screening for the optimal gene and functional gene sets related to breast cancer using differential co-expression and differential expression analysis.
Wang L; Ma H; Zhu L; Ma L; Cao L; Wei H; Xu J
Cancer Biomark; 2016; 17(4):463-471. PubMed ID: 27802197
[TBL] [Abstract][Full Text] [Related]
4. DECODE: an integrated differential co-expression and differential expression analysis of gene expression data.
Lui TW; Tsui NB; Chan LW; Wong CS; Siu PM; Yung BY
BMC Bioinformatics; 2015 May; 16():182. PubMed ID: 26026612
[TBL] [Abstract][Full Text] [Related]
5. TOP2A and CENPF are synergistic master regulators activated in cervical cancer.
Yu B; Chen L; Zhang W; Li Y; Zhang Y; Gao Y; Teng X; Zou L; Wang Q; Jia H; Liu X; Zheng H; Hou P; Yu H; Sun Y; Zhang Z; Zhang P; Zhang L
BMC Med Genomics; 2020 Oct; 13(1):145. PubMed ID: 33023625
[TBL] [Abstract][Full Text] [Related]
6. Identification of crucial aberrantly methylated and differentially expressed genes related to cervical cancer using an integrated bioinformatics analysis.
Ma X; Liu J; Wang H; Jiang Y; Wan Y; Xia Y; Cheng W
Biosci Rep; 2020 May; 40(5):. PubMed ID: 32368784
[TBL] [Abstract][Full Text] [Related]
7. Identification of a histone family gene signature for predicting the prognosis of cervical cancer patients.
Li X; Tian R; Gao H; Yang Y; Williams BRG; Gantier MP; McMillan NAJ; Xu D; Hu Y; Gao Y
Sci Rep; 2017 Nov; 7(1):16495. PubMed ID: 29184082
[TBL] [Abstract][Full Text] [Related]
8. Bioinformatics analysis of the transcriptional expression of minichromosome maintenance proteins as potential indicators of survival in patients with cervical cancer.
Wu B; Xi S
BMC Cancer; 2021 Aug; 21(1):928. PubMed ID: 34404366
[TBL] [Abstract][Full Text] [Related]
9. Predicting Hub Genes Associated with Cervical Cancer through Gene Co-Expression Networks.
Deng SP; Zhu L; Huang DS
IEEE/ACM Trans Comput Biol Bioinform; 2016; 13(1):27-35. PubMed ID: 26415208
[TBL] [Abstract][Full Text] [Related]
10. Subspace differential coexpression analysis: problem definition and a general approach.
Fang G; Kuang R; Pandey G; Steinbach M; Myers CL; Kumar V
Pac Symp Biocomput; 2010; ():145-56. PubMed ID: 19908367
[TBL] [Abstract][Full Text] [Related]
11. Combined Transcriptome and Proteome Analysis of Immortalized Human Keratinocytes Expressing Human Papillomavirus 16 (HPV16) Oncogenes Reveals Novel Key Factors and Networks in HPV-Induced Carcinogenesis.
Yang R; Klimentová J; Göckel-Krzikalla E; Ly R; Gmelin N; Hotz-Wagenblatt A; Řehulková H; Stulík J; Rösl F; Niebler M
mSphere; 2019 Mar; 4(2):. PubMed ID: 30918060
[TBL] [Abstract][Full Text] [Related]
12. Differential Coexpression Network Analysis for Gene Expression Data.
Liu BH
Methods Mol Biol; 2018; 1754():155-165. PubMed ID: 29536442
[TBL] [Abstract][Full Text] [Related]
13. 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]
14. The Role of Significantly Deregulated MicroRNAs in Recurrent Cervical Cancer Based on Bioinformatic Analysis of the Cancer Genome Atlas Data.
Liu H; Liu L; Zhu H
J Comput Biol; 2019 Apr; 26(4):387-395. PubMed ID: 30762435
[TBL] [Abstract][Full Text] [Related]
15. Pathogenic Network Analysis Predicts Candidate Genes for Cervical Cancer.
Zhang YX; Zhao YL
Comput Math Methods Med; 2016; 2016():3186051. PubMed ID: 27034707
[TBL] [Abstract][Full Text] [Related]
16. A three miRNAs signature predicts survival in cervical cancer using bioinformatics analysis.
Liang B; Li Y; Wang T
Sci Rep; 2017 Jul; 7(1):5624. PubMed ID: 28717180
[TBL] [Abstract][Full Text] [Related]
17. Integrated analysis of long non‑coding RNA competing interactions revealed potential biomarkers in cervical cancer: Based on a public database.
Wu WJ; Shen Y; Sui J; Li CY; Yang S; Xu SY; Zhang M; Yin LH; Pu YP; Liang GY
Mol Med Rep; 2018 Jun; 17(6):7845-7858. PubMed ID: 29620291
[TBL] [Abstract][Full Text] [Related]
18. Co-expression network analysis identified atypical chemokine receptor 1 (ACKR1) association with lymph node metastasis and prognosis in cervical cancer.
Liu J; Li S; Lin L; Jiang Y; Wan Y; Zhou S; Cheng W
Cancer Biomark; 2020; 27(2):213-223. PubMed ID: 32083574
[TBL] [Abstract][Full Text] [Related]
19. Understanding the transcriptional regulation of cervix cancer using microarray gene expression data and promoter sequence analysis of a curated gene set.
Srivastava P; Mangal M; Agarwal SM
Gene; 2014 Feb; 535(2):233-8. PubMed ID: 24291025
[TBL] [Abstract][Full Text] [Related]
20. A microRNA-Messenger RNA Regulatory Network and Its Prognostic Value in Cervical Cancer.
Liu J; Yang J; Gao F; Li S; Nie S; Meng H; Sun R; Wan Y; Jiang Y; Ma X; Cheng W
DNA Cell Biol; 2020 Jul; 39(7):1328-1346. PubMed ID: 32456463
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
