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基于贝叶斯网络的活动水平分配的 RFU 衍生 LR。

RFU derived LRs for activity level assignments using Bayesian Networks.

机构信息

Forensic Genetics Research Group, Oslo University Hospital, Oslo, Norway; Department of Clinical Medicine, University of Oslo, Oslo, Norway.

Forensic Genetics Research Group, Oslo University Hospital, Oslo, Norway.

出版信息

Forensic Sci Int Genet. 2022 Jan;56:102608. doi: 10.1016/j.fsigen.2021.102608. Epub 2021 Oct 21.

Abstract

A comparative study has been carried out, comparing two different methods to estimate activity level likelihood ratios (LR) using Bayesian Networks. The first method uses the sub-source likelihood ratio (logLR) 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 (M) 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.

摘要

已经进行了一项比较研究,比较了两种使用贝叶斯网络估计活动水平似然比(LR)的不同方法。第一种方法使用子源似然比(logLR)作为“质量指标”。然而,这种方法受到了批评,因为它可能会因为等位基因比例在人群中的差异而引入潜在的偏差。引入了一种替代方法,该方法基于 DNA 谱的总 RFU,使用从定量概率基因分型软件(EuroForMix)计算得出的混合比例(M)进行调整。直接转移数据的贝叶斯逻辑回归表明,这两种方法是可比的。差异归因于抽样误差和二次转移数据的小样本量。贝叶斯方法通过考虑抽样误差来促进比较研究;它可以很容易地扩展到比较不同的方法。

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