190 related articles for article (PubMed ID: 29170526)
21. An unsupervised learning approach to find ovarian cancer genes through integration of biological data.
Ma C; Chen Y; Wilkins D; Chen X; Zhang J
BMC Genomics; 2015; 16 Suppl 9(Suppl 9):S3. PubMed ID: 26328548
[TBL] [Abstract][Full Text] [Related]
22. A Five-Gene Expression Signature Predicts Ovarian Cancer Metastasis.
Gu Y; Zhang S
Crit Rev Eukaryot Gene Expr; 2021; 31(5):41-50. PubMed ID: 34591389
[TBL] [Abstract][Full Text] [Related]
23. Systematic prediction of key genes for ovarian cancer by co-expression network analysis.
Wang M; Wang J; Liu J; Zhu L; Ma H; Zou J; Wu W; Wang K
J Cell Mol Med; 2020 Jun; 24(11):6298-6307. PubMed ID: 32319226
[TBL] [Abstract][Full Text] [Related]
24. Identification of a key glioblastoma candidate gene, FUBP3, based on weighted gene co-expression network analysis.
Li J; Zhang Z; Guo K; Wu S; Guo C; Zhang X; Wang Z
BMC Neurol; 2022 Apr; 22(1):139. PubMed ID: 35413821
[TBL] [Abstract][Full Text] [Related]
25. LNDriver: identifying driver genes by integrating mutation and expression data based on gene-gene interaction network.
Wei PJ; Zhang D; Xia J; Zheng CH
BMC Bioinformatics; 2016 Dec; 17(Suppl 17):467. PubMed ID: 28155630
[TBL] [Abstract][Full Text] [Related]
26. Gene-microRNA network module analysis for ovarian cancer.
Zhang S; Ng MK
BMC Syst Biol; 2016 Dec; 10(Suppl 4):117. PubMed ID: 28155675
[TBL] [Abstract][Full Text] [Related]
27. Distinguishing between driver and passenger mutations in individual cancer genomes by network enrichment analysis.
Merid SK; Goranskaya D; Alexeyenko A
BMC Bioinformatics; 2014 Sep; 15(1):308. PubMed ID: 25236784
[TBL] [Abstract][Full Text] [Related]
28. Integrated analysis of recurrent properties of cancer genes to identify novel drivers.
D'Antonio M; Ciccarelli FD
Genome Biol; 2013 May; 14(5):R52. PubMed ID: 23718799
[TBL] [Abstract][Full Text] [Related]
29. Identification of modules and hub genes associated with platinum-based chemotherapy resistance and treatment response in ovarian cancer by weighted gene co-expression network analysis.
Zhang L; Zhang X; Fan S; Zhang Z
Medicine (Baltimore); 2019 Nov; 98(44):e17803. PubMed ID: 31689861
[TBL] [Abstract][Full Text] [Related]
30. Improving existing analysis pipeline to identify and analyze cancer driver genes using multi-omics data.
Nguyen QH; Le DH
Sci Rep; 2020 Nov; 10(1):20521. PubMed ID: 33239644
[TBL] [Abstract][Full Text] [Related]
31. Molecular profiling of mucinous epithelial ovarian cancer by weighted gene co-expression network analysis.
Zhang GH; Chen MM; Kai JY; Ma Q; Zhong AL; Xie SH; Zheng H; Wang YC; Tong Y; Lu RQ; Guo L
Gene; 2019 Aug; 709():56-64. PubMed ID: 31108164
[TBL] [Abstract][Full Text] [Related]
32. Integration of multi-omics data to mine cancer-related gene modules.
Li P; Guo M; Sun B
J Bioinform Comput Biol; 2019 Dec; 17(6):1950038. PubMed ID: 32019413
[TBL] [Abstract][Full Text] [Related]
33. Identifying mutated driver pathways in cancer by integrating multi-omics data.
Wu J; Cai Q; Wang J; Liao Y
Comput Biol Chem; 2019 Jun; 80():159-167. PubMed ID: 30959272
[TBL] [Abstract][Full Text] [Related]
34. The identification of six risk genes for ovarian cancer platinum response based on global network algorithm and verification analysis.
Xing L; Mi W; Zhang Y; Tian S; Zhang Y; Qi R; Lou G; Zhang C
J Cell Mol Med; 2020 Sep; 24(17):9839-9852. PubMed ID: 32762026
[TBL] [Abstract][Full Text] [Related]
35. DriverRWH: discovering cancer driver genes by random walk on a gene mutation hypergraph.
Wang C; Shi J; Cai J; Zhang Y; Zheng X; Zhang N
BMC Bioinformatics; 2022 Jul; 23(1):277. PubMed ID: 35831792
[TBL] [Abstract][Full Text] [Related]
36. Cancer core modules identification through genomic and transcriptomic changes correlation detection at network level.
Li W; Wang R; Bai L; Yan Z; Sun Z
BMC Syst Biol; 2012 Jun; 6():64. PubMed ID: 22691569
[TBL] [Abstract][Full Text] [Related]
37. Depicting the genetic architecture of pediatric cancers through an integrative gene network approach.
Savary C; Kim A; Lespagnol A; Gandemer V; Pellier I; Andrieu C; Pagès G; Galibert MD; Blum Y; de Tayrac M
Sci Rep; 2020 Jan; 10(1):1224. PubMed ID: 31988326
[TBL] [Abstract][Full Text] [Related]
38. Next-generation sequencing-based genomic profiling analysis reveals novel mutations for clinical diagnosis in Chinese primary epithelial ovarian cancer patients.
Zhang L; Luo M; Yang H; Zhu S; Cheng X; Qing C
J Ovarian Res; 2019 Feb; 12(1):19. PubMed ID: 30786925
[TBL] [Abstract][Full Text] [Related]
39. Network-based survival analysis reveals subnetwork signatures for predicting outcomes of ovarian cancer treatment.
Zhang W; Ota T; Shridhar V; Chien J; Wu B; Kuang R
PLoS Comput Biol; 2013; 9(3):e1002975. PubMed ID: 23555212
[TBL] [Abstract][Full Text] [Related]
40. A novel network control model for identifying personalized driver genes in cancer.
Guo WF; Zhang SW; Zeng T; Li Y; Gao J; Chen L
PLoS Comput Biol; 2019 Nov; 15(11):e1007520. PubMed ID: 31765387
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]