BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

148 related articles for article (PubMed ID: 28060710)

  • 1. A Review on Methods for Detecting SNP Interactions in High-Dimensional Genomic Data.
    Uppu S; Krishna A; Gopalan RP
    IEEE/ACM Trans Comput Biol Bioinform; 2018; 15(2):599-612. PubMed ID: 28060710
    [TBL] [Abstract][Full Text] [Related]  

  • 2. 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]  

  • 3. A deep hybrid model to detect multi-locus interacting SNPs in the presence of noise.
    Uppu S; Krishna A
    Int J Med Inform; 2018 Nov; 119():134-151. PubMed ID: 30342681
    [TBL] [Abstract][Full Text] [Related]  

  • 4. KDSNP: A kernel-based approach to detecting high-order SNP interactions.
    Kodama K; Saigo H
    J Bioinform Comput Biol; 2016 Oct; 14(5):1644003. PubMed ID: 27806683
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Genome-wide association data classification and SNPs selection using two-stage quality-based Random Forests.
    Nguyen TT; Huang J; Wu Q; Nguyen T; Li M
    BMC Genomics; 2015; 16 Suppl 2(Suppl 2):S5. PubMed ID: 25708662
    [TBL] [Abstract][Full Text] [Related]  

  • 6. [Advances in development of gene-gene interaction analysis methods based on SNP data: a review].
    Luan YZ; Zuo XY; Liu K; Li G; Rao SQ
    Yi Chuan; 2013 Dec; 35(12):1331-9. PubMed ID: 24645342
    [TBL] [Abstract][Full Text] [Related]  

  • 7. A whole-genome simulator capable of modeling high-order epistasis for complex disease.
    Yang W; Gu CC
    Genet Epidemiol; 2013 Nov; 37(7):686-94. PubMed ID: 24114848
    [TBL] [Abstract][Full Text] [Related]  

  • 8. SNPranker 2.0: a gene-centric data mining tool for diseases associated SNP prioritization in GWAS.
    Merelli I; Calabria A; Cozzi P; Viti F; Mosca E; Milanesi L
    BMC Bioinformatics; 2013; 14 Suppl 1(Suppl 1):S9. PubMed ID: 23369106
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Detection of gene x gene interactions in genome-wide association studies of human population data.
    Musani SK; Shriner D; Liu N; Feng R; Coffey CS; Yi N; Tiwari HK; Allison DB
    Hum Hered; 2007; 63(2):67-84. PubMed ID: 17283436
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Next-generation analysis of cataracts: determining knowledge driven gene-gene interactions using biofilter, and gene-environment interactions using the Phenx Toolkit*.
    Pendergrass SA; Verma SS; Hall MA; Holzinger ER; Moore CB; Wallace JR; Dudek SM; Huggins W; Kitchner T; Waudby C; Berg R; Mccarty CA; Ritchie MD
    Pac Symp Biocomput; 2015; ():495-505. PubMed ID: 25741542
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Review: High-performance computing to detect epistasis in genome scale data sets.
    Upton A; Trelles O; Cornejo-GarcĂ­a JA; Perkins JR
    Brief Bioinform; 2016 May; 17(3):368-79. PubMed ID: 26272945
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Functional regression method for whole genome eQTL epistasis analysis with sequencing data.
    Xu K; Jin L; Xiong M
    BMC Genomics; 2017 May; 18(1):385. PubMed ID: 28521784
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Revisiting genome-wide association studies from statistical modelling to machine learning.
    Sun S; Dong B; Zou Q
    Brief Bioinform; 2021 Jul; 22(4):. PubMed ID: 33126243
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Genotype distribution-based inference of collective effects in genome-wide association studies: insights to age-related macular degeneration disease mechanism.
    Woo HJ; Yu C; Kumar K; Gold B; Reifman J
    BMC Genomics; 2016 Aug; 17(1):695. PubMed ID: 27576376
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Gene-Gene Interactions Detection Using a Two-stage Model.
    Wang Z; Sul JH; Snir S; Lozano JA; Eskin E
    J Comput Biol; 2015 Jun; 22(6):563-76. PubMed ID: 25871811
    [TBL] [Abstract][Full Text] [Related]  

  • 16. FIFS: A data mining method for informative marker selection in high dimensional population genomic data.
    Kavakiotis I; Samaras P; Triantafyllidis A; Vlahavas I
    Comput Biol Med; 2017 Nov; 90():146-154. PubMed ID: 28992453
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Approximate Bayesian neural networks in genomic prediction.
    Waldmann P
    Genet Sel Evol; 2018 Dec; 50(1):70. PubMed ID: 30577737
    [TBL] [Abstract][Full Text] [Related]  

  • 18. INTERSNP: genome-wide interaction analysis guided by a priori information.
    Herold C; Steffens M; Brockschmidt FF; Baur MP; Becker T
    Bioinformatics; 2009 Dec; 25(24):3275-81. PubMed ID: 19837719
    [TBL] [Abstract][Full Text] [Related]  

  • 19. 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]  

  • 20. Pathways of distinction analysis: a new technique for multi-SNP analysis of GWAS data.
    Braun R; Buetow K
    PLoS Genet; 2011 Jun; 7(6):e1002101. PubMed ID: 21695280
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 8.