234 related articles for article (PubMed ID: 35620468)
21. 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]
22. The Integrative Method Based on the Module-Network for Identifying Driver Genes in Cancer Subtypes.
Lu X; Li X; Liu P; Qian X; Miao Q; Peng S
Molecules; 2018 Jan; 23(2):. PubMed ID: 29364829
[TBL] [Abstract][Full Text] [Related]
23. PRODIGY: personalized prioritization of driver genes.
Dinstag G; Shamir R
Bioinformatics; 2020 Mar; 36(6):1831-1839. PubMed ID: 31681944
[TBL] [Abstract][Full Text] [Related]
24. 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]
25. Personalized Driver Gene Prediction Using Graph Convolutional Networks with Conditional Random Fields.
Wei PJ; Zhu AD; Cao R; Zheng C
Biology (Basel); 2024 Mar; 13(3):. PubMed ID: 38534453
[TBL] [Abstract][Full Text] [Related]
26. Exomes of Ductal Luminal Breast Cancer Patients from Southwest Colombia: Gene Mutational Profile and Related Expression Alterations.
Cortes-Urrea C; Bueno-Gutiérrez F; Solarte M; Guevara-Burbano M; Tobar-Tosse F; Vélez-Varela PE; Bonilla JC; Barreto G; Velasco-Medina J; Moreno PA; Rivas JL
Biomolecules; 2020 Apr; 10(5):. PubMed ID: 32365829
[TBL] [Abstract][Full Text] [Related]
27. Oncogenes and tumor suppressor genes: comparative genomics and network perspectives.
Zhu K; Liu Q; Zhou Y; Tao C; Zhao Z; Sun J; Xu H
BMC Genomics; 2015; 16 Suppl 7(Suppl 7):S8. PubMed ID: 26099335
[TBL] [Abstract][Full Text] [Related]
28. New Insight into microRNA Functions in Cancer: Oncogene-microRNA-Tumor Suppressor Gene Network.
Zhou K; Liu M; Cao Y
Front Mol Biosci; 2017; 4():46. PubMed ID: 28736730
[TBL] [Abstract][Full Text] [Related]
29. PhenoDriver: interpretable framework for studying personalized phenotype-associated driver genes in breast cancer.
Li Y; Zhang SW; Xie MY; Zhang T
Brief Bioinform; 2023 Sep; 24(5):. PubMed ID: 37738403
[TBL] [Abstract][Full Text] [Related]
30. 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]
31. Preliminary evaluation of deep learning for first-line diagnostic prediction of tumor mutational status.
Morel LO; Derangère V; Arnould L; Ladoire S; Vinçon N
Sci Rep; 2023 Apr; 13(1):6927. PubMed ID: 37117277
[TBL] [Abstract][Full Text] [Related]
32. Accurate Prediction of Cancer Prognosis by Exploiting Patient-Specific Cancer Driver Genes.
Lee S; Jung H; Park J; Ahn J
Int J Mol Sci; 2023 Mar; 24(7):. PubMed ID: 37047418
[TBL] [Abstract][Full Text] [Related]
33. Comparison of different functional prediction scores using a gene-based permutation model for identifying cancer driver genes.
Nono AD; Chen K; Liu X
BMC Med Genomics; 2019 Jan; 12(Suppl 1):22. PubMed ID: 30704472
[TBL] [Abstract][Full Text] [Related]
34. 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]
35. Bioinformatics analysis of thousands of TCGA tumors to determine the involvement of epigenetic regulators in human cancer.
Gnad F; Doll S; Manning G; Arnott D; Zhang Z
BMC Genomics; 2015; 16 Suppl 8(Suppl 8):S5. PubMed ID: 26110843
[TBL] [Abstract][Full Text] [Related]
36. LOTUS: A single- and multitask machine learning algorithm for the prediction of cancer driver genes.
Collier O; Stoven V; Vert JP
PLoS Comput Biol; 2019 Sep; 15(9):e1007381. PubMed ID: 31568528
[TBL] [Abstract][Full Text] [Related]
37. 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]
38. Interpreting pathways to discover cancer driver genes with Moonlight.
Colaprico A; Olsen C; Bailey MH; Odom GJ; Terkelsen T; Silva TC; Olsen AV; Cantini L; Zinovyev A; Barillot E; Noushmehr H; Bertoli G; Castiglioni I; Cava C; Bontempi G; Chen XS; Papaleo E
Nat Commun; 2020 Jan; 11(1):69. PubMed ID: 31900418
[TBL] [Abstract][Full Text] [Related]
39. Exome-Scale Discovery of Hotspot Mutation Regions in Human Cancer Using 3D Protein Structure.
Tokheim C; Bhattacharya R; Niknafs N; Gygax DM; Kim R; Ryan M; Masica DL; Karchin R
Cancer Res; 2016 Jul; 76(13):3719-31. PubMed ID: 27197156
[TBL] [Abstract][Full Text] [Related]
40. deepDriver: Predicting Cancer Driver Genes Based on Somatic Mutations Using Deep Convolutional Neural Networks.
Luo P; Ding Y; Lei X; Wu FX
Front Genet; 2019; 10():13. PubMed ID: 30761181
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]
