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RFU derived LRs for activity level assignments using Bayesian Networks

Gill, Peter ; Bleka, Øyvind ; Fonneløp, Ane Elida

Forensic science international : genetics, 2022-01, Vol.56, p.102608-102608, Article 102608 [Periódico revisado por pares]

Netherlands: Elsevier B.V

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  • Título:
    RFU derived LRs for activity level assignments using Bayesian Networks
  • Autor: Gill, Peter ; Bleka, Øyvind ; Fonneløp, Ane Elida
  • Assuntos: ALTRaPht ; Bayes Theorem ; Bayesian Network ; Direct transfer ; DNA Fingerprinting ; EuroForMix ; Evidence evaluation ; Humans ; Likelihood Functions ; Likelihood ratio (LR) ; Microsatellite Repeats ; Mixtures ; Secondary transfer ; Software
  • É parte de: Forensic science international : genetics, 2022-01, Vol.56, p.102608-102608, Article 102608
  • Notas: ObjectType-Article-1
    SourceType-Scholarly Journals-1
    ObjectType-Feature-2
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  • Descrição: A comparative study has been carried out, comparing two different methods to estimate activity level likelihood ratios (LRa) using Bayesian Networks. The first method uses the sub-source likelihood ratio (log10LRϕ) as a ‘quality indicator’. However, this has been criticised as introducing potential bias from population differences in allelic proportions. An alternative method has been introduced that is based upon the total RFU of a DNA profile that is adjusted using the mixture proportion (Mx) which is calculated from quantitative probabilistic genotyping software (EuroForMix). Bayesian logistic regressions of direct transfer data showed that the two methods were comparable. Differences were attributed to sampling error, and small sample sizes of secondary transfer data. The Bayesian approach facilitates comparative studies by taking account of sampling error; it can easily be extended to compare different methods. •Probabilities of direct and secondary transfer are modelled by logistic regression.•Two different methods (RFU vs sub-source LR) are compared.•A method to calibrate RFU against sub-source LR measurements is introduced.
  • Editor: Netherlands: Elsevier B.V
  • Idioma: Inglês;Norueguês
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  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

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