BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

217 related articles for article (PubMed ID: 25625964)

  • 1. NOCIt: a computational method to infer the number of contributors to DNA samples analyzed by STR genotyping.
    Swaminathan H; Grgicak CM; Medard M; Lun DS
    Forensic Sci Int Genet; 2015 May; 16():172-180. PubMed ID: 25625964
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Inferring the Number of Contributors to Complex DNA Mixtures Using Three Methods: Exploring the Limits of Low-Template DNA Interpretation.
    Alfonse LE; Tejada G; Swaminathan H; Lun DS; Grgicak CM
    J Forensic Sci; 2017 Mar; 62(2):308-316. PubMed ID: 27907229
    [TBL] [Abstract][Full Text] [Related]  

  • 3. A series of developmental validation tests for Number of Contributors platforms: Exemplars using NOCIt and a neural network.
    Valtl J; Mönich UJ; Lun DS; Kelley J; Grgicak CM
    Forensic Sci Int Genet; 2021 Sep; 54():102556. PubMed ID: 34225042
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Statistical modeling of STR capillary electrophoresis signal.
    Karkar S; Alfonse LE; Grgicak CM; Lun DS
    BMC Bioinformatics; 2019 Dec; 20(Suppl 16):584. PubMed ID: 31787097
    [TBL] [Abstract][Full Text] [Related]  

  • 5. A large-scale validation of NOCIt's a posteriori probability of the number of contributors and its integration into forensic interpretation pipelines.
    Grgicak CM; Karkar S; Yearwood-Garcia X; Alfonse LE; Duffy KR; Lun DS
    Forensic Sci Int Genet; 2020 Jul; 47():102296. PubMed ID: 32339916
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Determining the number of contributors to DNA mixtures in the low-template regime: Exploring the impacts of sampling and detection effects.
    Norsworthy S; Lun DS; Grgicak CM
    Leg Med (Tokyo); 2018 May; 32():1-8. PubMed ID: 29453054
    [TBL] [Abstract][Full Text] [Related]  

  • 7. The a posteriori probability of the number of contributors when conditioned on an assumed contributor.
    Grgicak CM; Duffy KR; Lun DS
    Forensic Sci Int Genet; 2021 Sep; 54():102563. PubMed ID: 34284325
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Identifying the most likely contributors to a Y-STR mixture using the discrete Laplace method.
    Andersen MM; Eriksen PS; Mogensen HS; Morling N
    Forensic Sci Int Genet; 2015 Mar; 15():76-83. PubMed ID: 25303788
    [TBL] [Abstract][Full Text] [Related]  

  • 9. CEESIt: A computational tool for the interpretation of STR mixtures.
    Swaminathan H; Garg A; Grgicak CM; Medard M; Lun DS
    Forensic Sci Int Genet; 2016 May; 22():149-160. PubMed ID: 26946255
    [TBL] [Abstract][Full Text] [Related]  

  • 10. NGS-based likelihood ratio for identifying contributors in two- and three-person DNA mixtures.
    Chan Mun Wei J; Zhao Z; Li SC; Ng YK
    Comput Biol Chem; 2018 Jun; 74():428-433. PubMed ID: 29625871
    [TBL] [Abstract][Full Text] [Related]  

  • 11. TrueAllele(®) Genotype Identification on DNA Mixtures Containing up to Five Unknown Contributors.
    Perlin MW; Hornyak JM; Sugimoto G; Miller KW
    J Forensic Sci; 2015 Jul; 60(4):857-68. PubMed ID: 26189920
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Automated estimation of the number of contributors in autosomal short tandem repeat profiles using a machine learning approach.
    Benschop CCG; van der Linden J; Hoogenboom J; Ypma R; Haned H
    Forensic Sci Int Genet; 2019 Nov; 43():102150. PubMed ID: 31476660
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Internal validation of STRmix™ for the interpretation of single source and mixed DNA profiles.
    Moretti TR; Just RS; Kehl SC; Willis LE; Buckleton JS; Bright JA; Taylor DA; Onorato AJ
    Forensic Sci Int Genet; 2017 Jul; 29():126-144. PubMed ID: 28504203
    [TBL] [Abstract][Full Text] [Related]  

  • 14. A large-scale dataset of single and mixed-source short tandem repeat profiles to inform human identification strategies: PROVEDIt.
    Alfonse LE; Garrett AD; Lun DS; Duffy KR; Grgicak CM
    Forensic Sci Int Genet; 2018 Jan; 32():62-70. PubMed ID: 29091906
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Least-square deconvolution: a framework for interpreting short tandem repeat mixtures.
    Wang T; Xue N; Birdwell JD
    J Forensic Sci; 2006 Nov; 51(6):1284-97. PubMed ID: 17199614
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Estimation of the number of contributors of theoretical mixture profiles based on allele counting: Does increasing the number of loci increase success rate of estimates?
    Dembinski GM; Sobieralski C; Picard CJ
    Forensic Sci Int Genet; 2018 Mar; 33():24-32. PubMed ID: 29175725
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Development and validation of open-source software for DNA mixture interpretation based on a quantitative continuous model.
    Manabe S; Morimoto C; Hamano Y; Fujimoto S; Tamaki K
    PLoS One; 2017; 12(11):e0188183. PubMed ID: 29149210
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Massively parallel sequencing of 17 commonly used forensic autosomal STRs and amelogenin with small amplicons.
    Kim EH; Lee HY; Yang IS; Jung SE; Yang WI; Shin KJ
    Forensic Sci Int Genet; 2016 May; 22():1-7. PubMed ID: 26799314
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Identifying contributors of DNA mixtures by means of quantitative information of STR typing.
    Tvedebrink T; Eriksen PS; Mogensen HS; Morling N
    J Comput Biol; 2012 Jul; 19(7):887-902. PubMed ID: 21210742
    [TBL] [Abstract][Full Text] [Related]  

  • 20. An assessment of the information content of likelihood ratios derived from complex mixtures.
    Marsden CD; Rudin N; Inman K; Lohmueller KE
    Forensic Sci Int Genet; 2016 May; 22():64-72. PubMed ID: 26851613
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 11.