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 *

240 related articles for article (PubMed ID: 20568290)

  • 1. Identifying genetic interactions in genome-wide data using Bayesian networks.
    Jiang X; Barmada MM; Visweswaran S
    Genet Epidemiol; 2010 Sep; 34(6):575-81. PubMed ID: 20568290
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Learning genetic epistasis using Bayesian network scoring criteria.
    Jiang X; Neapolitan RE; Barmada MM; Visweswaran S
    BMC Bioinformatics; 2011 Mar; 12():89. PubMed ID: 21453508
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Genetic studies of complex human diseases: characterizing SNP-disease associations using Bayesian networks.
    Han B; Chen XW; Talebizadeh Z; Xu H
    BMC Syst Biol; 2012; 6 Suppl 3(Suppl 3):S14. PubMed ID: 23281790
    [TBL] [Abstract][Full Text] [Related]  

  • 4. A Bayesian method for identifying genetic interactions.
    Visweswaran S; Wong AK; Barmada MM
    AMIA Annu Symp Proc; 2009 Nov; 2009():673-7. PubMed ID: 20351939
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Cuckoo search epistasis: a new method for exploring significant genetic interactions.
    Aflakparast M; Salimi H; Gerami A; Dubé MP; Visweswaran S; Masoudi-Nejad A
    Heredity (Edinb); 2014 Jun; 112(6):666-74. PubMed ID: 24549111
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Detecting purely epistatic multi-locus interactions by an omnibus permutation test on ensembles of two-locus analyses.
    Wongseree W; Assawamakin A; Piroonratana T; Sinsomros S; Limwongse C; Chaiyaratana N
    BMC Bioinformatics; 2009 Sep; 10():294. PubMed ID: 19761607
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Mining pure, strict epistatic interactions from high-dimensional datasets: ameliorating the curse of dimensionality.
    Jiang X; Neapolitan RE
    PLoS One; 2012; 7(10):e46771. PubMed ID: 23071633
    [TBL] [Abstract][Full Text] [Related]  

  • 8. A Bayesian model for detection of high-order interactions among genetic variants in genome-wide association studies.
    Wang J; Joshi T; Valliyodan B; Shi H; Liang Y; Nguyen HT; Zhang J; Xu D
    BMC Genomics; 2015 Nov; 16():1011. PubMed ID: 26607428
    [TBL] [Abstract][Full Text] [Related]  

  • 9. bNEAT: a Bayesian network method for detecting epistatic interactions in genome-wide association studies.
    Han B; Chen XW
    BMC Genomics; 2011; 12 Suppl 2(Suppl 2):S9. PubMed ID: 21989368
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Searching Genome-Wide Multi-Locus Associations for Multiple Diseases Based on Bayesian Inference.
    Guo X; Zhang J; Cai Z; Du DZ; Pan Y
    IEEE/ACM Trans Comput Biol Bioinform; 2017; 14(3):600-610. PubMed ID: 26887006
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Evaluating the detection ability of a range of epistasis detection methods on simulated data for pure and impure epistatic models.
    Russ D; Williams JA; Cardoso VR; Bravo-Merodio L; Pendleton SC; Aziz F; Acharjee A; Gkoutos GV
    PLoS One; 2022; 17(2):e0263390. PubMed ID: 35180244
    [TBL] [Abstract][Full Text] [Related]  

  • 12. SNPHarvester: a filtering-based approach for detecting epistatic interactions in genome-wide association studies.
    Yang C; He Z; Wan X; Yang Q; Xue H; Yu W
    Bioinformatics; 2009 Feb; 25(4):504-11. PubMed ID: 19098029
    [TBL] [Abstract][Full Text] [Related]  

  • 13. KNN-MDR: a learning approach for improving interactions mapping performances in genome wide association studies.
    Abo Alchamlat S; Farnir F
    BMC Bioinformatics; 2017 Mar; 18(1):184. PubMed ID: 28327091
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Grid-based stochastic search for hierarchical gene-gene interactions in population-based genetic studies of common human diseases.
    Moore JH; Andrews PC; Olson RS; Carlson SE; Larock CR; Bulhoes MJ; O'Connor JP; Greytak EM; Armentrout SL
    BioData Min; 2017; 10():19. PubMed ID: 28572842
    [TBL] [Abstract][Full Text] [Related]  

  • 15. A fast algorithm for learning epistatic genomic relationships.
    Jiang X; Neapolitan RE; Barmada MM; Visweswaran S; Cooper GF
    AMIA Annu Symp Proc; 2010 Nov; 2010():341-5. PubMed ID: 21346997
    [TBL] [Abstract][Full Text] [Related]  

  • 16. A Markov blanket-based method for detecting causal SNPs in GWAS.
    Han B; Park M; Chen XW
    BMC Bioinformatics; 2010 Apr; 11 Suppl 3(Suppl 3):S5. PubMed ID: 20438652
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Cloud computing for detecting high-order genome-wide epistatic interaction via dynamic clustering.
    Guo X; Meng Y; Yu N; Pan Y
    BMC Bioinformatics; 2014 Apr; 15():102. PubMed ID: 24717145
    [TBL] [Abstract][Full Text] [Related]  

  • 18. 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]  

  • 19. GEP-EpiSeeker: a gene expression programming-based method for epistatic interaction detection in genome-wide association studies.
    Peng YZ; Lin Y; Huang Y; Li Y; Luo G; Liao J
    BMC Genomics; 2021 Dec; 22(Suppl 1):910. PubMed ID: 34930147
    [TBL] [Abstract][Full Text] [Related]  

  • 20. 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]  

    [Next]    [New Search]
    of 12.