These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

186 related articles for article (PubMed ID: 34112883)

  • 1. Evaluating machine learning methodologies for identification of cancer driver genes.
    Malebary SJ; Khan YD
    Sci Rep; 2021 Jun; 11(1):12281. PubMed ID: 34112883
    [TBL] [Abstract][Full Text] [Related]  

  • 2. 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]  

  • 3. 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]  

  • 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. 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]  

  • 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. 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]  

  • 9. Machine learning random forest for predicting oncosomatic variant NGS analysis.
    Pellegrino E; Jacques C; Beaufils N; Nanni I; Carlioz A; Metellus P; Ouafik L
    Sci Rep; 2021 Nov; 11(1):21820. PubMed ID: 34750410
    [TBL] [Abstract][Full Text] [Related]  

  • 10. 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]  

  • 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. 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]  

  • 13. 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]  

  • 14. Identification of Cancer Driver Modules Based on Graph Clustering from Multiomics Data.
    Zhang W; Wang SL; Liu Y
    J Comput Biol; 2021 Oct; 28(10):1007-1020. PubMed ID: 34529511
    [No Abstract]   [Full Text] [Related]  

  • 15. CDMPred: a tool for predicting cancer driver missense mutations with high-quality passenger mutations.
    Wang L; Sun H; Yue Z; Xia J; Li X
    PeerJ; 2024; 12():e17991. PubMed ID: 39253604
    [TBL] [Abstract][Full Text] [Related]  

  • 16. A novel network control model for identifying personalized driver genes in cancer.
    Guo WF; Zhang SW; Zeng T; Li Y; Gao J; Chen L
    PLoS Comput Biol; 2019 Nov; 15(11):e1007520. PubMed ID: 31765387
    [TBL] [Abstract][Full Text] [Related]  

  • 17. A new molecular signature method for prediction of driver cancer pathways from transcriptional data.
    Rykunov D; Beckmann ND; Li H; Uzilov A; Schadt EE; Reva B
    Nucleic Acids Res; 2016 Jun; 44(11):e110. PubMed ID: 27098033
    [TBL] [Abstract][Full Text] [Related]  

  • 18. IMI-driver: Integrating multi-level gene networks and multi-omics for cancer driver gene identification.
    Shi P; Han J; Zhang Y; Li G; Zhou X
    PLoS Comput Biol; 2024 Aug; 20(8):e1012389. PubMed ID: 39186807
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Comprehensive evaluation of computational methods for predicting cancer driver genes.
    Shi X; Teng H; Shi L; Bi W; Wei W; Mao F; Sun Z
    Brief Bioinform; 2022 Mar; 23(2):. PubMed ID: 35037014
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Identification of Latent Oncogenes with a Network Embedding Method and Random Forest.
    Zhao R; Hu B; Chen L; Zhou B
    Biomed Res Int; 2020; 2020():5160396. PubMed ID: 33029511
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 10.