BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

191 related articles for article (PubMed ID: 37551622)

  • 1. 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]  

  • 2. 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]  

  • 3. 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]  

  • 4. 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]  

  • 5. Uncovering driver genes in breast cancer through an innovative machine learning mutational analysis method.
    Taheri G; Habibi M
    Comput Biol Med; 2024 Mar; 171():108234. PubMed ID: 38430742
    [TBL] [Abstract][Full Text] [Related]  

  • 6. 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]  

  • 7. 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]  

  • 8. 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]  

  • 9. Integrating mutation and gene expression cross-sectional data to infer cancer progression.
    Fleck JL; Pavel AB; Cassandras CG
    BMC Syst Biol; 2016 Jan; 10():12. PubMed ID: 26810975
    [TBL] [Abstract][Full Text] [Related]  

  • 10. DriverGroup: a novel method for identifying driver gene groups.
    Pham VVH; Liu L; Bracken CP; Goodall GJ; Li J; Le TD
    Bioinformatics; 2020 Dec; 36(Suppl_2):i583-i591. PubMed ID: 33381812
    [TBL] [Abstract][Full Text] [Related]  

  • 11. 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]  

  • 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. Cross-species DNA copy number analyses identifies multiple 1q21-q23 subtype-specific driver genes for breast cancer.
    Silva GO; He X; Parker JS; Gatza ML; Carey LA; Hou JP; Moulder SL; Marcom PK; Ma J; Rosen JM; Perou CM
    Breast Cancer Res Treat; 2015 Jul; 152(2):347-56. PubMed ID: 26109346
    [TBL] [Abstract][Full Text] [Related]  

  • 14. 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]  

  • 15. Two-stage-vote ensemble framework based on integration of mutation data and gene interaction network for uncovering driver genes.
    Kan Y; Jiang L; Guo Y; Tang J; Guo F
    Brief Bioinform; 2022 Jan; 23(1):. PubMed ID: 34791034
    [TBL] [Abstract][Full Text] [Related]  

  • 16. The cancer driver genes IDH1/2, JARID1C/ KDM5C, and UTX/ KDM6A: crosstalk between histone demethylation and hypoxic reprogramming in cancer metabolism.
    Chang S; Yim S; Park H
    Exp Mol Med; 2019 Jun; 51(6):1-17. PubMed ID: 31221981
    [TBL] [Abstract][Full Text] [Related]  

  • 17. 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]  

  • 18. 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]  

  • 19. 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]  

  • 20. 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]  

    [Next]    [New Search]
    of 10.