471 related articles for article (PubMed ID: 30253099)
41. Mutational patterns in oncogenes and tumour suppressors.
Baeissa HM; Benstead-Hume G; Richardson CJ; Pearl FM
Biochem Soc Trans; 2016 Jun; 44(3):925-31. PubMed ID: 27284061
[TBL] [Abstract][Full Text] [Related]
42. Effects of Multi-Omics Characteristics on Identification of Driver Genes Using Machine Learning Algorithms.
Li F; Chu X; Dai L; Wang J; Liu J; Shang J
Genes (Basel); 2022 Apr; 13(5):. PubMed ID: 35627101
[TBL] [Abstract][Full Text] [Related]
43. IDENTIFY CANCER DRIVER GENES THROUGH SHARED MENDELIAN DISEASE PATHOGENIC VARIANTS AND CANCER SOMATIC MUTATIONS.
Ma M; Wang C; Glicksberg BS; Schadt EE; Li SD; Chen R
Pac Symp Biocomput; 2017; 22():473-484. PubMed ID: 27896999
[TBL] [Abstract][Full Text] [Related]
44. A workflow to study mechanistic indicators for driver gene prediction with Moonlight.
Nourbakhsh M; Saksager A; Tom N; Chen XS; Colaprico A; Olsen C; Tiberti M; Papaleo E
Brief Bioinform; 2023 Sep; 24(5):. PubMed ID: 37551622
[TBL] [Abstract][Full Text] [Related]
45. Ontology-based prediction of cancer driver genes.
Althubaiti S; Karwath A; Dallol A; Noor A; Alkhayyat SS; Alwassia R; Mineta K; Gojobori T; Beggs AD; Schofield PN; Gkoutos GV; Hoehndorf R
Sci Rep; 2019 Nov; 9(1):17405. PubMed ID: 31757986
[TBL] [Abstract][Full Text] [Related]
46. Dr.Nod: computational framework for discovery of regulatory non-coding drivers in tissue-matched distal regulatory elements.
Tomkova M; Tomek J; Chow J; McPherson JD; Segal DJ; Hormozdiari F
Nucleic Acids Res; 2023 Feb; 51(4):e23. PubMed ID: 36625266
[TBL] [Abstract][Full Text] [Related]
47. A new machine learning method for cancer mutation analysis.
Habibi M; Taheri G
PLoS Comput Biol; 2022 Oct; 18(10):e1010332. PubMed ID: 36251702
[TBL] [Abstract][Full Text] [Related]
48. SB Driver Analysis: a Sleeping Beauty cancer driver analysis framework for identifying and prioritizing experimentally actionable oncogenes and tumor suppressors.
Newberg JY; Black MA; Jenkins NA; Copeland NG; Mann KM; Mann MB
Nucleic Acids Res; 2018 Sep; 46(16):e94. PubMed ID: 29846651
[TBL] [Abstract][Full Text] [Related]
49. 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]
50. Driver gene mutations based clustering of tumors: methods and applications.
Zhang W; Flemington EK; Zhang K
Bioinformatics; 2018 Jul; 34(13):i404-i411. PubMed ID: 29950003
[TBL] [Abstract][Full Text] [Related]
51. The effects of mutational processes and selection on driver mutations across cancer types.
Temko D; Tomlinson IPM; Severini S; Schuster-Böckler B; Graham TA
Nat Commun; 2018 May; 9(1):1857. PubMed ID: 29748584
[TBL] [Abstract][Full Text] [Related]
52. Dynamic changes of driver genes' mutations across clinical stages in nine cancer types.
Li X
Cancer Med; 2016 Jul; 5(7):1556-65. PubMed ID: 26992457
[TBL] [Abstract][Full Text] [Related]
53. OncodriveROLE classifies cancer driver genes in loss of function and activating mode of action.
Schroeder MP; Rubio-Perez C; Tamborero D; Gonzalez-Perez A; Lopez-Bigas N
Bioinformatics; 2014 Sep; 30(17):i549-55. PubMed ID: 25161246
[TBL] [Abstract][Full Text] [Related]
54. Identifying cancer type specific oncogenes and tumor suppressors using limited size data.
Pavel AB; Vasile CI
J Bioinform Comput Biol; 2016 Dec; 14(6):1650031. PubMed ID: 27712196
[TBL] [Abstract][Full Text] [Related]
55. Synonymous mutations frequently act as driver mutations in human cancers.
Supek F; Miñana B; Valcárcel J; Gabaldón T; Lehner B
Cell; 2014 Mar; 156(6):1324-1335. PubMed ID: 24630730
[TBL] [Abstract][Full Text] [Related]
56. InDEP: an interpretable machine learning approach to predict cancer driver genes from multi-omics data.
Yang H; Liu Y; Yang Y; Li D; Wang Z
Brief Bioinform; 2023 Sep; 24(5):. PubMed ID: 37649392
[TBL] [Abstract][Full Text] [Related]
57. Identification of coding and non-coding mutational hotspots in cancer genomes.
Piraino SW; Furney SJ
BMC Genomics; 2017 Jan; 18(1):17. PubMed ID: 28056774
[TBL] [Abstract][Full Text] [Related]
58. 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]
59. Network-based prediction approach for cancer-specific driver missense mutations using a graph neural network.
Hatano N; Kamada M; Kojima R; Okuno Y
BMC Bioinformatics; 2023 Oct; 24(1):383. PubMed ID: 37817080
[TBL] [Abstract][Full Text] [Related]
60. QuaDMutNetEx: a method for detecting cancer driver genes with low mutation frequency.
Bokhari Y; Alhareeri A; Arodz T
BMC Bioinformatics; 2020 Mar; 21(1):122. PubMed ID: 32293263
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]