BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

225 related articles for article (PubMed ID: 26581171)

  • 1. Exploring preferred amino acid mutations in cancer genes: Applications to identify potential drug targets.
    Anoosha P; Sakthivel R; Michael Gromiha M
    Biochim Biophys Acta; 2016 Feb; 1862(2):155-65. PubMed ID: 26581171
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Predicting the functional consequences of somatic missense mutations found in tumors.
    Carter H; Karchin R
    Methods Mol Biol; 2014; 1101():135-59. PubMed ID: 24233781
    [TBL] [Abstract][Full Text] [Related]  

  • 3. 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]  

  • 4. Gene characteristics predicting missense, nonsense and frameshift mutations in tumor samples.
    Gorlov IP; Pikielny CW; Frost HR; Her SC; Cole MD; Strohbehn SD; Wallace-Bradley D; Kimmel M; Gorlova OY; Amos CI
    BMC Bioinformatics; 2018 Nov; 19(1):430. PubMed ID: 30453881
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Driver Missense Mutation Identification Using Feature Selection and Model Fusion.
    Soliman AT; Meng T; Chen SC; Iyengar SS; Iyengar P; Yordy J; Shyu ML
    J Comput Biol; 2015 Dec; 22(12):1075-85. PubMed ID: 26402258
    [TBL] [Abstract][Full Text] [Related]  

  • 6. 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]  

  • 7. Assessment of computational methods for predicting the effects of missense mutations in human cancers.
    Gnad F; Baucom A; Mukhyala K; Manning G; Zhang Z
    BMC Genomics; 2013; 14 Suppl 3(Suppl 3):S7. PubMed ID: 23819521
    [TBL] [Abstract][Full Text] [Related]  

  • 8. An analysis of substitution, deletion and insertion mutations in cancer genes.
    Iengar P
    Nucleic Acids Res; 2012 Aug; 40(14):6401-13. PubMed ID: 22492711
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Understanding the impacts of missense mutations on structures and functions of human cancer-related genes: A preliminary computational analysis of the COSMIC Cancer Gene Census.
    Malhotra S; Alsulami AF; Heiyun Y; Ochoa BM; Jubb H; Forbes S; Blundell TL
    PLoS One; 2019; 14(7):e0219935. PubMed ID: 31323058
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Cancer Driver Log (CanDL): Catalog of Potentially Actionable Cancer Mutations.
    Damodaran S; Miya J; Kautto E; Zhu E; Samorodnitsky E; Datta J; Reeser JW; Roychowdhury S
    J Mol Diagn; 2015 Sep; 17(5):554-9. PubMed ID: 26320871
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Predicting the functional consequences of cancer-associated amino acid substitutions.
    Shihab HA; Gough J; Cooper DN; Day IN; Gaunt TR
    Bioinformatics; 2013 Jun; 29(12):1504-10. PubMed ID: 23620363
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Use of signals of positive and negative selection to distinguish cancer genes and passenger genes.
    Bányai L; Trexler M; Kerekes K; Csuka O; Patthy L
    Elife; 2021 Jan; 10():. PubMed ID: 33427197
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Statistical analysis of pathogenicity of somatic mutations in cancer.
    Greenman C; Wooster R; Futreal PA; Stratton MR; Easton DF
    Genetics; 2006 Aug; 173(4):2187-98. PubMed ID: 16783027
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Discrimination of driver and passenger mutations in epidermal growth factor receptor in cancer.
    Anoosha P; Huang LT; Sakthivel R; Karunagaran D; Gromiha MM
    Mutat Res; 2015 Oct; 780():24-34. PubMed ID: 26264175
    [TBL] [Abstract][Full Text] [Related]  

  • 15. An analysis of mutational signatures of synonymous mutations across 15 cancer types.
    Bin Y; Wang X; Zhao L; Wen P; Xia J
    BMC Med Genet; 2019 Dec; 20(Suppl 2):190. PubMed ID: 31815613
    [TBL] [Abstract][Full Text] [Related]  

  • 16. 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]  

  • 17. Systematic Prioritization of Druggable Mutations in ∼5000 Genomes Across 16 Cancer Types Using a Structural Genomics-based Approach.
    Zhao J; Cheng F; Wang Y; Arteaga CL; Zhao Z
    Mol Cell Proteomics; 2016 Feb; 15(2):642-56. PubMed ID: 26657081
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Mutation distribution in the von Willebrand factor gene related to the different von Willebrand disease (VWD) types in a cohort of VWD patients.
    Yadegari H; Driesen J; Pavlova A; Biswas A; Hertfelder HJ; Oldenburg J
    Thromb Haemost; 2012 Oct; 108(4):662-71. PubMed ID: 22871923
    [TBL] [Abstract][Full Text] [Related]  

  • 19. e-Driver: a novel method to identify protein regions driving cancer.
    Porta-Pardo E; Godzik A
    Bioinformatics; 2014 Nov; 30(21):3109-14. PubMed ID: 25064568
    [TBL] [Abstract][Full Text] [Related]  

  • 20. 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]  

    [Next]    [New Search]
    of 12.