BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

63 related articles for article (PubMed ID: 24246292)

  • 1. Random forests on distance matrices for imaging genetics studies.
    Sim A; Tsagkrasoulis D; Montana G
    Stat Appl Genet Mol Biol; 2013 Dec; 12(6):757-86. PubMed ID: 24246292
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Random forests on Hadoop for genome-wide association studies of multivariate neuroimaging phenotypes.
    Wang Y; Goh W; Wong L; Montana G;
    BMC Bioinformatics; 2013; 14 Suppl 16(Suppl 16):S6. PubMed ID: 24564704
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Joint association discovery and diagnosis of Alzheimer's disease by supervised heterogeneous multiview learning.
    Zhe S; Xu Z; Qi Y; Yu P;
    Pac Symp Biocomput; 2014; ():300-11. PubMed ID: 24297556
    [TBL] [Abstract][Full Text] [Related]  

  • 4. SNP selection and classification of genome-wide SNP data using stratified sampling random forests.
    Wu Q; Ye Y; Liu Y; Ng MK
    IEEE Trans Nanobioscience; 2012 Sep; 11(3):216-27. PubMed ID: 22987127
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Statistical geometry based prediction of nonsynonymous SNP functional effects using random forest and neuro-fuzzy classifiers.
    Barenboim M; Masso M; Vaisman II; Jamison DC
    Proteins; 2008 Jun; 71(4):1930-9. PubMed ID: 18186470
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Genome-wide association analysis of secondary imaging phenotypes from the Alzheimer's disease neuroimaging initiative study.
    Zhu W; Yuan Y; Zhang J; Zhou F; Knickmeyer RC; Zhu H;
    Neuroimage; 2017 Feb; 146():983-1002. PubMed ID: 27717770
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Deep mixed model for marginal epistasis detection and population stratification correction in genome-wide association studies.
    Wang H; Yue T; Yang J; Wu W; Xing EP
    BMC Bioinformatics; 2019 Dec; 20(Suppl 23):656. PubMed ID: 31881907
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Neighbourhood approximation using randomized forests.
    Konukoglu E; Glocker B; Zikic D; Criminisi A
    Med Image Anal; 2013 Oct; 17(7):790-804. PubMed ID: 23725639
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Equivalence of kernel machine regression and kernel distance covariance for multidimensional phenotype association studies.
    Hua WY; Ghosh D
    Biometrics; 2015 Sep; 71(3):812-20. PubMed ID: 25939365
    [TBL] [Abstract][Full Text] [Related]  

  • 10. A comparison of analytical methods for genetic association studies.
    Motsinger-Reif AA; Reif DM; Fanelli TJ; Ritchie MD
    Genet Epidemiol; 2008 Dec; 32(8):767-78. PubMed ID: 18561203
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Higher order interactions: detection of epistasis using machine learning and evolutionary computation.
    Nelson RM; Kierczak M; Carlborg O
    Methods Mol Biol; 2013; 1019():499-518. PubMed ID: 23756908
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Evaluating the ability of tree-based methods and logistic regression for the detection of SNP-SNP interaction.
    García-Magariños M; López-de-Ullibarri I; Cao R; Salas A
    Ann Hum Genet; 2009 May; 73(Pt 3):360-9. PubMed ID: 19291098
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Modeling X Chromosome Data Using Random Forests: Conquering Sex Bias.
    Winham SJ; Jenkins GD; Biernacka JM
    Genet Epidemiol; 2016 Feb; 40(2):123-32. PubMed ID: 26639183
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Epistasis analysis using artificial intelligence.
    Moore JH; Hill DP
    Methods Mol Biol; 2015; 1253():327-46. PubMed ID: 25403541
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Linear trend tests for case-control genetic association that incorporate random phenotype and genotype misclassification error.
    Gordon D; Haynes C; Yang Y; Kramer PL; Finch SJ
    Genet Epidemiol; 2007 Dec; 31(8):853-70. PubMed ID: 17565750
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Comparison of approaches for machine-learning optimization of neural networks for detecting gene-gene interactions in genetic epidemiology.
    Motsinger-Reif AA; Dudek SM; Hahn LW; Ritchie MD
    Genet Epidemiol; 2008 May; 32(4):325-40. PubMed ID: 18265411
    [TBL] [Abstract][Full Text] [Related]  

  • 17. IndOR: a new statistical procedure to test for SNP-SNP epistasis in genome-wide association studies.
    Emily M
    Stat Med; 2012 Sep; 31(21):2359-73. PubMed ID: 22711278
    [TBL] [Abstract][Full Text] [Related]  

  • 18. A flexible computational framework for detecting, characterizing, and interpreting statistical patterns of epistasis in genetic studies of human disease susceptibility.
    Moore JH; Gilbert JC; Tsai CT; Chiang FT; Holden T; Barney N; White BC
    J Theor Biol; 2006 Jul; 241(2):252-61. PubMed ID: 16457852
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Application and comparison of classification algorithms for recognition of Alzheimer's disease in electrical brain activity (EEG).
    Lehmann C; Koenig T; Jelic V; Prichep L; John RE; Wahlund LO; Dodge Y; Dierks T
    J Neurosci Methods; 2007 Apr; 161(2):342-50. PubMed ID: 17156848
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Pair-wise multifactor dimensionality reduction method to detect gene-gene interactions in a case-control study.
    He H; Oetting WS; Brott MJ; Basu S
    Hum Hered; 2010; 69(1):60-70. PubMed ID: 19797910
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 4.