171 related articles for article (PubMed ID: 33568049)
21. Aggregated network centrality shows non-random structure of genomic and proteomic networks.
Halder AK; Denkiewicz M; Sengupta K; Basu S; Plewczynski D
Methods; 2020 Oct; 181-182():5-14. PubMed ID: 31740366
[TBL] [Abstract][Full Text] [Related]
22. Prioritization of cancer driver gene with prize-collecting steiner tree by introducing an edge weighted strategy in the personalized gene interaction network.
Zhang SW; Wang ZN; Li Y; Guo WF
BMC Bioinformatics; 2022 Aug; 23(1):341. PubMed ID: 35974311
[TBL] [Abstract][Full Text] [Related]
23. Exploring gene-patient association to identify personalized cancer driver genes by linear neighborhood propagation.
Huang Y; Chen F; Sun H; Zhong C
BMC Bioinformatics; 2024 Jan; 25(1):34. PubMed ID: 38254011
[TBL] [Abstract][Full Text] [Related]
24. Variability of Betweenness Centrality and Its Effect on Identifying Essential Genes.
Durón C; Pan Y; Gutmann DH; Hardin J; Radunskaya A
Bull Math Biol; 2019 Sep; 81(9):3655-3673. PubMed ID: 30350013
[TBL] [Abstract][Full Text] [Related]
25. 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]
26. Gene Prioritization and Network Topology Analysis of Targeted Genes for Acquired Taxane Resistance by Meta-Analysis.
Kim D; Lee YS; Kim JK; Kim SY
Crit Rev Eukaryot Gene Expr; 2019; 29(6):581-597. PubMed ID: 32422012
[TBL] [Abstract][Full Text] [Related]
27. DEOD: uncovering dominant effects of cancer-driver genes based on a partial covariance selection method.
Amgalan B; Lee H
Bioinformatics; 2015 Aug; 31(15):2452-60. PubMed ID: 25819079
[TBL] [Abstract][Full Text] [Related]
28. Identification of driver copy number alterations in diverse cancer types and application in drug repositioning.
Zhou W; Zhao Z; Wang R; Han Y; Wang C; Yang F; Han Y; Liang H; Qi L; Wang C; Guo Z; Gu Y
Mol Oncol; 2017 Oct; 11(10):1459-1474. PubMed ID: 28719033
[TBL] [Abstract][Full Text] [Related]
29. 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]
30. Prioritizing Cancer Genes Based on an Improved Random Walk Method.
Wei PJ; Wu FX; Xia J; Su Y; Wang J; Zheng CH
Front Genet; 2020; 11():377. PubMed ID: 32411180
[TBL] [Abstract][Full Text] [Related]
31. driveR: a novel method for prioritizing cancer driver genes using somatic genomics data.
Ülgen E; Sezerman OU
BMC Bioinformatics; 2021 May; 22(1):263. PubMed ID: 34030627
[TBL] [Abstract][Full Text] [Related]
32. ABCDE: Approximating Betweenness-Centrality ranking with progressive-DropEdge.
Mirakyan M
PeerJ Comput Sci; 2021; 7():e699. PubMed ID: 34604524
[TBL] [Abstract][Full Text] [Related]
33. BMRF-MI: integrative identification of protein interaction network by modeling the gene dependency.
Shi X; Wang X; Shajahan A; Hilakivi-Clarke L; Clarke R; Xuan J
BMC Genomics; 2015; 16 Suppl 7(Suppl 7):S10. PubMed ID: 26099273
[TBL] [Abstract][Full Text] [Related]
34. A network-based method for identifying cancer driver genes based on node control centrality.
Li F; Li H; Shang J; Liu JX; Dai L; Liu X; Li Y
Exp Biol Med (Maywood); 2023 Feb; 248(3):232-241. PubMed ID: 36573462
[TBL] [Abstract][Full Text] [Related]
35. ProcessDriver: A computational pipeline to identify copy number drivers and associated disrupted biological processes in cancer.
Baur B; Bozdag S
Genomics; 2017 Jul; 109(3-4):233-240. PubMed ID: 28438487
[TBL] [Abstract][Full Text] [Related]
36. Integration of multiple networks and pathways identifies cancer driver genes in pan-cancer analysis.
Cava C; Bertoli G; Colaprico A; Olsen C; Bontempi G; Castiglioni I
BMC Genomics; 2018 Jan; 19(1):25. PubMed ID: 29304754
[TBL] [Abstract][Full Text] [Related]
37. GenHITS: A network science approach to driver gene detection in human regulatory network using gene's influence evaluation.
Akhavan-Safar M; Teimourpour B; Kargari M
J Biomed Inform; 2021 Feb; 114():103661. PubMed ID: 33326867
[TBL] [Abstract][Full Text] [Related]
38. CBNA: A control theory based method for identifying coding and non-coding cancer drivers.
Pham VVH; Liu L; Bracken CP; Goodall GJ; Long Q; Li J; Le TD
PLoS Comput Biol; 2019 Dec; 15(12):e1007538. PubMed ID: 31790386
[TBL] [Abstract][Full Text] [Related]
39. Clone temporal centrality measures for incomplete sequences of graph snapshots.
Hanke M; Foraita R
BMC Bioinformatics; 2017 May; 18(1):261. PubMed ID: 28511665
[TBL] [Abstract][Full Text] [Related]
40. Identification of critical microRNA gene targets in cervical cancer using network properties.
Sharma G; Agarwal SM
Microrna; 2014; 3(1):37-44. PubMed ID: 25069511
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]