235 related articles for article (PubMed ID: 35620468)
41. Discovering potential cancer driver genes by an integrated network-based approach.
Shi K; Gao L; Wang B
Mol Biosyst; 2016 Aug; 12(9):2921-31. PubMed ID: 27426053
[TBL] [Abstract][Full Text] [Related]
42. TP53_PROF: a machine learning model to predict impact of missense mutations in TP53.
Ben-Cohen G; Doffe F; Devir M; Leroy B; Soussi T; Rosenberg S
Brief Bioinform; 2022 Mar; 23(2):. PubMed ID: 35043155
[TBL] [Abstract][Full Text] [Related]
43. Cumulative haploinsufficiency and triplosensitivity drive aneuploidy patterns and shape the cancer genome.
Davoli T; Xu AW; Mengwasser KE; Sack LM; Yoon JC; Park PJ; Elledge SJ
Cell; 2013 Nov; 155(4):948-62. PubMed ID: 24183448
[TBL] [Abstract][Full Text] [Related]
44. DriverMP enables improved identification of cancer driver genes.
Liu Y; Han J; Kong T; Xiao N; Mei Q; Liu J
Gigascience; 2022 Dec; 12():. PubMed ID: 38091511
[TBL] [Abstract][Full Text] [Related]
45. Identification of mutated core cancer modules by integrating somatic mutation, copy number variation, and gene expression data.
Zhang J; Zhang S; Wang Y; Zhang XS
BMC Syst Biol; 2013; 7 Suppl 2(Suppl 2):S4. PubMed ID: 24565034
[TBL] [Abstract][Full Text] [Related]
46. VarWalker: personalized mutation network analysis of putative cancer genes from next-generation sequencing data.
Jia P; Zhao Z
PLoS Comput Biol; 2014 Feb; 10(2):e1003460. PubMed ID: 24516372
[TBL] [Abstract][Full Text] [Related]
47. Evaluating the evaluation of cancer driver genes.
Tokheim CJ; Papadopoulos N; Kinzler KW; Vogelstein B; Karchin R
Proc Natl Acad Sci U S A; 2016 Dec; 113(50):14330-14335. PubMed ID: 27911828
[TBL] [Abstract][Full Text] [Related]
48. 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]
49. Identification of constrained cancer driver genes based on mutation timing.
Sakoparnig T; Fried P; Beerenwinkel N
PLoS Comput Biol; 2015 Jan; 11(1):e1004027. PubMed ID: 25569148
[TBL] [Abstract][Full Text] [Related]
50. Understanding oncogenicity of cancer driver genes and mutations in the cancer genomics era.
Porta-Pardo E; Valencia A; Godzik A
FEBS Lett; 2020 Dec; 594(24):4233-4246. PubMed ID: 32239503
[TBL] [Abstract][Full Text] [Related]
51. Concordance of copy number loss and down-regulation of tumor suppressor genes: a pan-cancer study.
Zhao M; Zhao Z
BMC Genomics; 2016 Aug; 17 Suppl 7(Suppl 7):532. PubMed ID: 27556634
[TBL] [Abstract][Full Text] [Related]
52. Machine learning methods for prediction of cancer driver genes: a survey paper.
Andrades R; Recamonde-Mendoza M
Brief Bioinform; 2022 May; 23(3):. PubMed ID: 35323900
[TBL] [Abstract][Full Text] [Related]
53. 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]
54. 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]
55. Inferring causal genomic alterations in breast cancer using gene expression data.
Tran LM; Zhang B; Zhang Z; Zhang C; Xie T; Lamb JR; Dai H; Schadt EE; Zhu J
BMC Syst Biol; 2011 Aug; 5():121. PubMed ID: 21806811
[TBL] [Abstract][Full Text] [Related]
56. 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]
57. DRdriver: identifying drug resistance driver genes using individual-specific gene regulatory network.
Huang YE; Zhou S; Liu H; Zhou X; Yuan M; Hou F; Chen S; Chen J; Wang L; Jiang W
Brief Bioinform; 2023 Mar; 24(2):. PubMed ID: 36869849
[TBL] [Abstract][Full Text] [Related]
58. Evaluating machine learning methodologies for identification of cancer driver genes.
Malebary SJ; Khan YD
Sci Rep; 2021 Jun; 11(1):12281. PubMed ID: 34112883
[TBL] [Abstract][Full Text] [Related]
59. Somatic Genomics and Clinical Features of Lung Adenocarcinoma: A Retrospective Study.
Shi J; Hua X; Zhu B; Ravichandran S; Wang M; Nguyen C; Brodie SA; Palleschi A; Alloisio M; Pariscenti G; Jones K; Zhou W; Bouk AJ; Boland J; Hicks B; Risch A; Bennett H; Luke BT; Song L; Duan J; Liu P; Kohno T; Chen Q; Meerzaman D; Marconett C; Laird-Offringa I; Mills I; Caporaso NE; Gail MH; Pesatori AC; Consonni D; Bertazzi PA; Chanock SJ; Landi MT
PLoS Med; 2016 Dec; 13(12):e1002162. PubMed ID: 27923066
[TBL] [Abstract][Full Text] [Related]
60. Epigenetically Silenced Candidate Tumor Suppressor Genes in Prostate Cancer: Identified by Modeling Methylation Stratification and Applied to Progression Prediction.
Zhang W; Flemington EK; Deng HW; Zhang K
Cancer Epidemiol Biomarkers Prev; 2019 Jan; 28(1):198-207. PubMed ID: 30262601
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]
