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 *

113 related articles for article (PubMed ID: 17425620)

  • 1. Application of multi-locus analytical methods to identify interacting loci in case-control studies.
    Vermeulen SH; Den Heijer M; Sham P; Knight J
    Ann Hum Genet; 2007 Sep; 71(Pt 5):689-700. PubMed ID: 17425620
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Power of multifactor dimensionality reduction and penalized logistic regression for detecting gene-gene interaction in a case-control study.
    He H; Oetting WS; Brott MJ; Basu S
    BMC Med Genet; 2009 Dec; 10():127. PubMed ID: 19961594
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Comparative analysis of methods for detecting interacting loci.
    Chen L; Yu G; Langefeld CD; Miller DJ; Guy RT; Raghuram J; Yuan X; Herrington DM; Wang Y
    BMC Genomics; 2011 Jul; 12():344. PubMed ID: 21729295
    [TBL] [Abstract][Full Text] [Related]  

  • 4. A novel survival multifactor dimensionality reduction method for detecting gene-gene interactions with application to bladder cancer prognosis.
    Gui J; Moore JH; Kelsey KT; Marsit CJ; Karagas MR; Andrew AS
    Hum Genet; 2011 Jan; 129(1):101-10. PubMed ID: 20981448
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Spatial rank-based multifactor dimensionality reduction to detect gene-gene interactions for multivariate phenotypes.
    Park M; Jeong HB; Lee JH; Park T
    BMC Bioinformatics; 2021 Oct; 22(1):480. PubMed ID: 34607566
    [TBL] [Abstract][Full Text] [Related]  

  • 6. A comparison of internal validation techniques for multifactor dimensionality reduction.
    Winham SJ; Slater AJ; Motsinger-Reif AA
    BMC Bioinformatics; 2010 Jul; 11():394. PubMed ID: 20650002
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Detecting multi-way epistasis in family-based association studies.
    Loucoubar C; Grant AV; Bureau JF; Casademont I; Bar NA; Bar-Hen A; Diop M; Faye J; Sarr FD; Badiane A; Tall A; Trape JF; Cliquet F; Schwikowski B; Lathrop M; Paul RE; Sakuntabhai A
    Brief Bioinform; 2017 May; 18(3):394-402. PubMed ID: 27178992
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Exploring the performance of Multifactor Dimensionality Reduction in large scale SNP studies and in the presence of genetic heterogeneity among epistatic disease models.
    Edwards TL; Lewis K; Velez DR; Dudek S; Ritchie MD
    Hum Hered; 2009; 67(3):183-92. PubMed ID: 19077437
    [TBL] [Abstract][Full Text] [Related]  

  • 9. A unified model based multifactor dimensionality reduction framework for detecting gene-gene interactions.
    Yu W; Lee S; Park T
    Bioinformatics; 2016 Sep; 32(17):i605-i610. PubMed ID: 27587680
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Model-based multifactor dimensionality reduction for detecting epistasis in case-control data in the presence of noise.
    Cattaert T; Calle ML; Dudek SM; Mahachie John JM; Van Lishout F; Urrea V; Ritchie MD; Van Steen K
    Ann Hum Genet; 2011 Jan; 75(1):78-89. PubMed ID: 21158747
    [TBL] [Abstract][Full Text] [Related]  

  • 11. An improved fuzzy set-based multifactor dimensionality reduction for detecting epistasis.
    Yang CH; Chuang LY; Lin YD
    Artif Intell Med; 2020 Jan; 102():101768. PubMed ID: 31980105
    [TBL] [Abstract][Full Text] [Related]  

  • 12. 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]  

  • 13. A Belief Degree-Associated Fuzzy Multifactor Dimensionality Reduction Framework for Epistasis Detection.
    Rahaman S; Wong KC
    Methods Mol Biol; 2021; 2212():307-323. PubMed ID: 33733364
    [TBL] [Abstract][Full Text] [Related]  

  • 14. MDR-ER: balancing functions for adjusting the ratio in risk classes and classification errors for imbalanced cases and controls using multifactor-dimensionality reduction.
    Yang CH; Lin YD; Chuang LY; Chen JB; Chang HW
    PLoS One; 2013; 8(11):e79387. PubMed ID: 24236125
    [TBL] [Abstract][Full Text] [Related]  

  • 15. 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]  

  • 16. DualWMDR: Detecting epistatic interaction with dual screening and multifactor dimensionality reduction.
    Cao X; Yu G; Ren W; Guo M; Wang J
    Hum Mutat; 2020 Mar; 41(3):719-734. PubMed ID: 31705708
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Multifactor dimensionality reduction-phenomics: a novel method to capture genetic heterogeneity with use of phenotypic variables.
    Mei H; Cuccaro ML; Martin ER
    Am J Hum Genet; 2007 Dec; 81(6):1251-61. PubMed ID: 17999363
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Power of multifactor dimensionality reduction for detecting gene-gene interactions in the presence of genotyping error, missing data, phenocopy, and genetic heterogeneity.
    Ritchie MD; Hahn LW; Moore JH
    Genet Epidemiol; 2003 Feb; 24(2):150-7. PubMed ID: 12548676
    [TBL] [Abstract][Full Text] [Related]  

  • 19. A simple and computationally efficient sampling approach to covariate adjustment for multifactor dimensionality reduction analysis of epistasis.
    Gui J; Andrew AS; Andrews P; Nelson HM; Kelsey KT; Karagas MR; Moore JH
    Hum Hered; 2010; 70(3):219-25. PubMed ID: 20924193
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Model-Based Multifactor Dimensionality Reduction to detect epistasis for quantitative traits in the presence of error-free and noisy data.
    Mahachie John JM; Van Lishout F; Van Steen K
    Eur J Hum Genet; 2011 Jun; 19(6):696-703. PubMed ID: 21407267
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 6.