BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

476 related articles for article (PubMed ID: 30253099)

  • 1. Machine Learning Classification and Structure-Functional Analysis of Cancer Mutations Reveal Unique Dynamic and Network Signatures of Driver Sites in Oncogenes and Tumor Suppressor Genes.
    Agajanian S; Odeyemi O; Bischoff N; Ratra S; Verkhivker GM
    J Chem Inf Model; 2018 Oct; 58(10):2131-2150. PubMed ID: 30253099
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Biophysical simulations and structure-based modeling of residue interaction networks in the tumor suppressor proteins reveal functional role of cancer mutation hotspots in molecular communication.
    Verkhivker GM
    Biochim Biophys Acta Gen Subj; 2019 Jan; 1863(1):210-225. PubMed ID: 30339916
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Integration of Random Forest Classifiers and Deep Convolutional Neural Networks for Classification and Biomolecular Modeling of Cancer Driver Mutations.
    Agajanian S; Oluyemi O; Verkhivker GM
    Front Mol Biosci; 2019; 6():44. PubMed ID: 31245384
    [TBL] [Abstract][Full Text] [Related]  

  • 4. 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]  

  • 5. DriverML: a machine learning algorithm for identifying driver genes in cancer sequencing studies.
    Han Y; Yang J; Qian X; Cheng WC; Liu SH; Hua X; Zhou L; Yang Y; Wu Q; Liu P; Lu Y
    Nucleic Acids Res; 2019 May; 47(8):e45. PubMed ID: 30773592
    [TBL] [Abstract][Full Text] [Related]  

  • 6. 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]  

  • 7. SomInaClust: detection of cancer genes based on somatic mutation patterns of inactivation and clustering.
    Van den Eynden J; Fierro AC; Verbeke LP; Marchal K
    BMC Bioinformatics; 2015 Apr; 16():125. PubMed ID: 25903787
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Why Are Some Driver Mutations Rare?
    Nussinov R; Tsai CJ; Jang H
    Trends Pharmacol Sci; 2019 Dec; 40(12):919-929. PubMed ID: 31699406
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Identification of new driver and passenger mutations within APOBEC-induced hotspot mutations in bladder cancer.
    Shi MJ; Meng XY; Fontugne J; Chen CL; Radvanyi F; Bernard-Pierrot I
    Genome Med; 2020 Sep; 12(1):85. PubMed ID: 32988402
    [TBL] [Abstract][Full Text] [Related]  

  • 10. 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]  

  • 11. 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]  

  • 12. A CATH domain functional family based approach to identify putative cancer driver genes and driver mutations.
    Ashford P; Pang CSM; Moya-García AA; Adeyelu T; Orengo CA
    Sci Rep; 2019 Jan; 9(1):263. PubMed ID: 30670742
    [TBL] [Abstract][Full Text] [Related]  

  • 13. The pan-cancer analysis of gain-of-functional mutations to identify the common oncogenic signatures in multiple cancers.
    Wee Y; Liu Y; Bhyan SB; Lu J; Zhao M
    Gene; 2019 May; 697():57-66. PubMed ID: 30796966
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Identification and analysis of mutational hotspots in oncogenes and tumour suppressors.
    Baeissa H; Benstead-Hume G; Richardson CJ; Pearl FMG
    Oncotarget; 2017 Mar; 8(13):21290-21304. PubMed ID: 28423505
    [TBL] [Abstract][Full Text] [Related]  

  • 15. A Novel Method for Identifying the Potential Cancer Driver Genes Based on Molecular Data Integration.
    Zhang W; Wang SL
    Biochem Genet; 2020 Feb; 58(1):16-39. PubMed ID: 31115714
    [TBL] [Abstract][Full Text] [Related]  

  • 16. In silico saturation mutagenesis of cancer genes.
    Muiños F; Martínez-Jiménez F; Pich O; Gonzalez-Perez A; Lopez-Bigas N
    Nature; 2021 Aug; 596(7872):428-432. PubMed ID: 34321661
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Frequent mutations in acetylation and ubiquitination sites suggest novel driver mechanisms of cancer.
    Narayan S; Bader GD; Reimand J
    Genome Med; 2016 May; 8(1):55. PubMed ID: 27175787
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Mutational landscape of RNA-binding proteins in human cancers.
    Neelamraju Y; Gonzalez-Perez A; Bhat-Nakshatri P; Nakshatri H; Janga SC
    RNA Biol; 2018 Jan; 15(1):115-129. PubMed ID: 29023197
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Multi-Omic Data Improve Prediction of Personalized Tumor Suppressors and Oncogenes.
    Sudhakar M; Rengaswamy R; Raman K
    Front Genet; 2022; 13():854190. PubMed ID: 35620468
    [TBL] [Abstract][Full Text] [Related]  

  • 20. 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]  

    [Next]    [New Search]
    of 24.