BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

162 related articles for article (PubMed ID: 29342229)

  • 1. A rapid epistatic mixed-model association analysis by linear retransformations of genomic estimated values.
    Ning C; Wang D; Kang H; Mrode R; Zhou L; Xu S; Liu JF
    Bioinformatics; 2018 Jun; 34(11):1817-1825. PubMed ID: 29342229
    [TBL] [Abstract][Full Text] [Related]  

  • 2. CMDR based differential evolution identifies the epistatic interaction in genome-wide association studies.
    Yang CH; Chuang LY; Lin YD
    Bioinformatics; 2017 Aug; 33(15):2354-2362. PubMed ID: 28379338
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Rapid epistatic mixed-model association studies by controlling multiple polygenic effects.
    Wang D; Tang H; Liu JF; Xu S; Zhang Q; Ning C
    Bioinformatics; 2020 Dec; 36(19):4833-4837. PubMed ID: 32614415
    [TBL] [Abstract][Full Text] [Related]  

  • 4. On the use of GBLUP and its extension for GWAS with additive and epistatic effects.
    Zhang J; Liu F; Reif JC; Jiang Y
    G3 (Bethesda); 2021 Jul; 11(7):. PubMed ID: 33871030
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Predictive rule inference for epistatic interaction detection in genome-wide association studies.
    Wan X; Yang C; Yang Q; Xue H; Tang NL; Yu W
    Bioinformatics; 2010 Jan; 26(1):30-7. PubMed ID: 19880365
    [TBL] [Abstract][Full Text] [Related]  

  • 6. GWIS--model-free, fast and exhaustive search for epistatic interactions in case-control GWAS.
    Goudey B; Rawlinson D; Wang Q; Shi F; Ferra H; Campbell RM; Stern L; Inouye MT; Ong CS; Kowalczyk A
    BMC Genomics; 2013; 14 Suppl 3(Suppl 3):S10. PubMed ID: 23819779
    [TBL] [Abstract][Full Text] [Related]  

  • 7. 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]  

  • 8. Prioritizing tests of epistasis through hierarchical representation of genomic redundancies.
    Cowman T; Koyutürk M
    Nucleic Acids Res; 2017 Aug; 45(14):e131. PubMed ID: 28605458
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Leveraging the genetic correlation between traits improves the detection of epistasis in genome-wide association studies.
    Stamp J; DenAdel A; Weinreich D; Crawford L
    G3 (Bethesda); 2023 Aug; 13(8):. PubMed ID: 37243672
    [TBL] [Abstract][Full Text] [Related]  

  • 10. WISH-R- a fast and efficient tool for construction of epistatic networks for complex traits and diseases.
    Carmelo VAO; Kogelman LJA; Madsen MB; Kadarmideen HN
    BMC Bioinformatics; 2018 Jul; 19(1):277. PubMed ID: 30064383
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Genomic prediction of fertility and calving traits in Holstein cattle based on models including epistatic genetic effects.
    Alves K; Brito LF; Schenkel FS
    J Anim Breed Genet; 2023 Sep; 140(5):568-581. PubMed ID: 37254293
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Fast detection of high-order epistatic interactions in genome-wide association studies using information theoretic measure.
    Leem S; Jeong HH; Lee J; Wee K; Sohn KA
    Comput Biol Chem; 2014 Jun; 50():19-28. PubMed ID: 24581733
    [TBL] [Abstract][Full Text] [Related]  

  • 13. 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]  

  • 14. 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]  

  • 15. 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]  

  • 16. Modeling Epistasis in Genomic Selection.
    Jiang Y; Reif JC
    Genetics; 2015 Oct; 201(2):759-68. PubMed ID: 26219298
    [TBL] [Abstract][Full Text] [Related]  

  • 17. CINOEDV: a co-information based method for detecting and visualizing n-order epistatic interactions.
    Shang J; Sun Y; Liu JX; Xia J; Zhang J; Zheng CH
    BMC Bioinformatics; 2016 May; 17(1):214. PubMed ID: 27184783
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Genome-wide hierarchical mixed model association analysis.
    Hao Z; Gao J; Song Y; Yang R; Liu D
    Brief Bioinform; 2021 Nov; 22(6):. PubMed ID: 34368830
    [TBL] [Abstract][Full Text] [Related]  

  • 19. SMMB: a stochastic Markov blanket framework strategy for epistasis detection in GWAS.
    Niel C; Sinoquet C; Dina C; Rocheleau G
    Bioinformatics; 2018 Aug; 34(16):2773-2780. PubMed ID: 29547902
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Estimation of pleiotropy between complex diseases using single-nucleotide polymorphism-derived genomic relationships and restricted maximum likelihood.
    Lee SH; Yang J; Goddard ME; Visscher PM; Wray NR
    Bioinformatics; 2012 Oct; 28(19):2540-2. PubMed ID: 22843982
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 9.