BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

111 related articles for article (PubMed ID: 34463035)

  • 1. Nonrandom missing data can bias Principal Component Analysis inference of population genetic structure.
    Yi X; Latch EK
    Mol Ecol Resour; 2022 Feb; 22(2):602-611. PubMed ID: 34463035
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Robust inference of population structure from next-generation sequencing data with systematic differences in sequencing.
    Liao P; Satten GA; Hu YJ
    Bioinformatics; 2018 Apr; 34(7):1157-1163. PubMed ID: 29186324
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Large-scale inference of population structure in presence of missingness using PCA.
    Meisner J; Liu S; Huang M; Albrechtsen A
    Bioinformatics; 2021 Jul; 37(13):1868-1875. PubMed ID: 33459779
    [TBL] [Abstract][Full Text] [Related]  

  • 4. The Impact of Nonrandom Missingness in Surveillance Data for Population-Level Summaries: Simulation Study.
    Weiss PS; Waller LA
    JMIR Public Health Surveill; 2022 Sep; 8(9):e37887. PubMed ID: 36083618
    [TBL] [Abstract][Full Text] [Related]  

  • 5. How do SNP ascertainment schemes and population demographics affect inferences about population history?
    McTavish EJ; Hillis DM
    BMC Genomics; 2015 Apr; 16(1):266. PubMed ID: 25887858
    [TBL] [Abstract][Full Text] [Related]  

  • 6. RADseq underestimates diversity and introduces genealogical biases due to nonrandom haplotype sampling.
    Arnold B; Corbett-Detig RB; Hartl D; Bomblies K
    Mol Ecol; 2013 Jun; 22(11):3179-90. PubMed ID: 23551379
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Missing data imputation via the expectation-maximization algorithm can improve principal component analysis aimed at deriving biomarker profiles and dietary patterns.
    Malan L; Smuts CM; Baumgartner J; Ricci C
    Nutr Res; 2020 Mar; 75():67-76. PubMed ID: 32035304
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Genotype-free estimation of allele frequencies reduces bias and improves demographic inference from RADSeq data.
    Warmuth VM; Ellegren H
    Mol Ecol Resour; 2019 May; 19(3):586-596. PubMed ID: 30633448
    [TBL] [Abstract][Full Text] [Related]  

  • 9. How "simple" methodological decisions affect interpretation of population structure based on reduced representation library DNA sequencing: A case study using the lake whitefish.
    Graham CF; Boreham DR; Manzon RG; Stott W; Wilson JY; Somers CM
    PLoS One; 2020; 15(1):e0226608. PubMed ID: 31978053
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Principal components analysis of population admixture.
    Ma J; Amos CI
    PLoS One; 2012; 7(7):e40115. PubMed ID: 22808102
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Massively parallel sequencing of 165 ancestry-informative SNPs and forensic biogeographical ancestry inference in three southern Chinese Sinitic/Tai-Kadai populations.
    He G; Liu J; Wang M; Zou X; Ming T; Zhu S; Yeh HY; Wang C; Wang Z; Hou Y
    Forensic Sci Int Genet; 2021 May; 52():102475. PubMed ID: 33561661
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Commonly used Hardy-Weinberg equilibrium filtering schemes impact population structure inferences using RADseq data.
    Pearman WS; Urban L; Alexander A
    Mol Ecol Resour; 2022 Oct; 22(7):2599-2613. PubMed ID: 35593534
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Inferring Population Structure and Admixture Proportions in Low-Depth NGS Data.
    Meisner J; Albrechtsen A
    Genetics; 2018 Oct; 210(2):719-731. PubMed ID: 30131346
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Genotyping-by-sequencing for estimating relatedness in nonmodel organisms: Avoiding the trap of precise bias.
    Attard CRM; Beheregaray LB; Möller LM
    Mol Ecol Resour; 2018 May; 18(3):381-390. PubMed ID: 29160928
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Genetic diversity analysis of highly incomplete SNP genotype data with imputations: an empirical assessment.
    Fu YB
    G3 (Bethesda); 2014 Mar; 4(5):891-900. PubMed ID: 24626289
    [TBL] [Abstract][Full Text] [Related]  

  • 16. High-dimensional principal component analysis with heterogeneous missingness.
    Zhu Z; Wang T; Samworth RJ
    J R Stat Soc Series B Stat Methodol; 2022 Nov; 84(5):2000-2031. PubMed ID: 37065873
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Exploring Population Structure with Admixture Models and Principal Component Analysis.
    Liu CC; Shringarpure S; Lange K; Novembre J
    Methods Mol Biol; 2020; 2090():67-86. PubMed ID: 31975164
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Principal Component Analyses (PCA)-based findings in population genetic studies are highly biased and must be reevaluated.
    Elhaik E
    Sci Rep; 2022 Aug; 12(1):14683. PubMed ID: 36038559
    [TBL] [Abstract][Full Text] [Related]  

  • 19. PCA-correlated SNPs for structure identification in worldwide human populations.
    Paschou P; Ziv E; Burchard EG; Choudhry S; Rodriguez-Cintron W; Mahoney MW; Drineas P
    PLoS Genet; 2007 Sep; 3(9):1672-86. PubMed ID: 17892327
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Efficient toolkit implementing best practices for principal component analysis of population genetic data.
    Privé F; Luu K; Blum MGB; McGrath JJ; Vilhjálmsson BJ
    Bioinformatics; 2020 Aug; 36(16):4449-4457. PubMed ID: 32415959
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 6.