PACE: Probabilistic Assessment for Contributor Estimation— A machine learning-based assessment of the number of contributors in DNA mixtures

Marciano, Michael A.; Adelman, Jonathan D.

doi:10.1016/j.fsigen.2016.11.006

Cited by 39 publications

(38 citation statements)

References 14 publications

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“…They estimate the probability of the observed alleles given various numbers of contributors and account for allele probabilities and the coancestry coefficient. NOCIt [7] adds a consideration of peak heights, and PACE [8] uses machine learning to assign probabilities to different numbers of contributors, and as such are likely to be the most informed tools.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

The effect of varying the number of contributors in the prosecution and alternate propositions

Buckleton

Bright

Cheng

et al. 2019

Forensic Science International: Genetics

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Using a simplified model, we examine the effect of varying the number of contributors in the prosecution and alternate propositions for a number of simulated examples. We compare the Slooten and Caliebe [1] solution, with several existing practices. Our own experience is that most laboratories, and ourselves, assign the number of contributors, N = n, by allele count and a manual examination of peak heights. The LRn for one or a very few values is calculated and typically one of these is presented, usually the most conservative. This gives an acceptable approximation. Reassessing the number of contributors if LR = 0 and adding one to N under both Hp and Ha to "fit" the POI may lead to a substantial overstatement of the LR. A more reasonable option is to allow optimisation of the assignment under Hp and Ha separately. We show that an additional contributor explained the single locus profile better when 0.51. PHR  This is pleasingly in line with current interpretation approaches. Collectively these trials, and the solid theoretical development, suggest that the Slooten and Caliebe approach preforms well.

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Section: Accepted Manuscriptmentioning

confidence: 99%

The effect of varying the number of contributors in the prosecution and alternate propositions

Buckleton

Bright

Cheng

et al. 2019

Forensic Science International: Genetics

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“…Under this process, a NoC is assigned to the profile based on the number of allelic peaks and their heights, often after consideration of artifacts. While most constrained NoC determinations are performed manually, there are software tools available to assist if needed, such as NOCIt based on Monte Carlo methods, PACE based on machine learning, and methods using Bayesian networks and maximum likelihood .…”

Section: Number Of Contributors (Noc)mentioning

confidence: 99%

The Probabilistic Genotyping Software STRmix: Utility and Evidence for its Validity

Buckleton

Bright

Gittelson³

et al. 2018

Journal of Forensic Sciences

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Forensic DNA interpretation is transitioning from manual interpretation based usually on binary decision-making toward computer-based systems that model the probability of the profile given different explanations for it, termed probabilistic genotyping (PG). Decision-making by laboratories to implement probability-based interpretation should be based on scientific principles for validity and information that supports its utility, such as criteria to support admissibility. The principles behind STRmix™ are outlined in this study and include standard mathematics and modeling of peak heights and variability in those heights. All PG methods generate a likelihood ratio (LR) and require the formulation of propositions. Principles underpinning formulations of propositions include the identification of reasonably assumed contributors. Substantial data have been produced that support precision, error rate, and reliability of PG, and in particular, STRmix™. A current issue is access to the code and quality processes used while coding. There are substantial data that describe the performance, strengths, and limitations of STRmix™, one of the available PG software.

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“…For example, probabilistic-based interpretation systems typically require an assumption on the number of contributors (NOC) [39,40]. As a result, work on NOC estimation and the NOC assumption [18,[41][42][43] has catalyzed the development of methods that manage this limitation [44][45][46][47]. Moreover, proposed interpretation schemes do not all use the same underlying probabilistic model.…”

Section: Introductionmentioning

confidence: 99%

A large-scale dataset of single and mixed-source short tandem repeat profiles to inform human identification strategies: PROVEDIt

Alfonse

Garrett

Lun

et al. 2018

Forensic Science International: Genetics

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DNA-based human identity testing is conducted by comparison of PCR-amplified polymorphic Short Tandem Repeat (STR) motifs from a known source with the STR profiles obtained from uncertain sources. Samples such as those found at crime scenes often result in signal that is a composite of incomplete STR profiles from an unknown number of unknown contributors, making interpretation an arduous task. To facilitate advancement in STR interpretation challenges we provide over 25,000 multiplex STR profiles produced from one to five known individuals at target levels ranging from one to 160 copies of DNA. The data, generated under 144 laboratory conditions, are classified by total copy number and contributor proportions. For the 70% of samples that were synthetically compromised, we report the level of DNA damage using quantitative and end-point PCR. In addition, we characterize the complexity of the signal by exploring the number of detected alleles in each profile.

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PACE: Probabilistic Assessment for Contributor Estimation— A machine learning-based assessment of the number of contributors in DNA mixtures

Cited by 39 publications

References 14 publications

The effect of varying the number of contributors in the prosecution and alternate propositions

The effect of varying the number of contributors in the prosecution and alternate propositions

The Probabilistic Genotyping Software STRmix: Utility and Evidence for its Validity

A large-scale dataset of single and mixed-source short tandem repeat profiles to inform human identification strategies: PROVEDIt

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