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Stefanović A, Šorgić D, Cvetković N, Antović A, Ilić G. Precision touch DNA sampling on plastic bag knots for improved profiling of packer and holder contributions. Forensic Sci Int Genet 2024; 71:103033. [PMID: 38522394 DOI: 10.1016/j.fsigen.2024.103033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 03/14/2024] [Accepted: 03/15/2024] [Indexed: 03/26/2024]
Abstract
In forensic DNA analysis, evidence sampling stands as a pivotal step setting the ground for the quality of the forensic profiling. The collection of touch DNA from objects, when guidelines are scarce or absent, is usually governed by ad hoc decisions based on the available case circumstances. In our laboratory, in the context of illicit drug-related crimes, similar objects are frequently encountered, offering an opportunity for the standardization of evidence treatment. This study aims to develop an effective method for sampling touch DNA from knots on plastic bags. We examine both the exposed and hidden areas of knots, considering the latter as "protected" zones less likely to accumulate biological material during subsequent handling. The study contrasts a single sample method (whole knot surface sampling, Method 1) with dual-sample methods that separate exterior (exposed) and interior (hidden) surfaces of the knot. Notably, our study consistently reveals higher DNA yields from exterior surfaces of the knots as opposed to interior samples. Importantly, our findings demonstrate that utilizing a single sample may produce DNA profiles that are not interpretable, while employing a dual-sample approach may allow for the differentiation between the genetic contributions of the person who tied the knot, the packer, from the person who held the package, the holder. We have refined the dual-sample method to reduce holder DNA in the interior sample while maintaining it on the exterior, also allowing the packer's DNA to be detected on both surfaces. We explore four dual-sample collection methods. Method 2 involves taking the first sample from the exterior and the second from the interior of an untied knot. Method 3 visually differentiates between the original exposed and hidden surfaces for precise sampling. Method 4 employs tools to open the knot for interior sampling. Method 5 uses Diamond dye to highlight cell-free DNA on both surfaces before sampling. In conclusion, this study not only clarifies the complex dynamics of touch DNA transfer and collection on plastic bag knots, but also offers insights into standardizing evidence collection in similar cases.
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Affiliation(s)
| | - Dejan Šorgić
- Institute of Legal Medicine, Bulevar Dr. Zorana Đinđića 81, Niš 18000, Serbia
| | - Nataša Cvetković
- Institute of Legal Medicine, Bulevar Dr. Zorana Đinđića 81, Niš 18000, Serbia
| | - Aleksandra Antović
- Institute of Legal Medicine, Bulevar Dr. Zorana Đinđića 81, Niš 18000, Serbia
| | - Goran Ilić
- Institute of Legal Medicine, Bulevar Dr. Zorana Đinđića 81, Niš 18000, Serbia
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2
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van Lierop S, Ramos D, Sjerps M, Ypma R. An overview of log likelihood ratio cost in forensic science - Where is it used and what values can we expect? Forensic Sci Int Synerg 2024; 8:100466. [PMID: 38645839 PMCID: PMC11031735 DOI: 10.1016/j.fsisyn.2024.100466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Revised: 03/07/2024] [Accepted: 03/29/2024] [Indexed: 04/23/2024]
Abstract
There is increasing support for reporting evidential strength as a likelihood ratio (LR) and increasing interest in (semi-)automated LR systems. The log-likelihood ratio cost (Cllr) is a popular metric for such systems, penalizing misleading LRs further from 1 more. Cllr = 0 indicates perfection while Cllr = 1 indicates an uninformative system. However, beyond this, what constitutes a "good" Cllr is unclear. Aiming to provide handles on when a Cllr is "good", we studied 136 publications on (semi-)automated LR systems. Results show Cllr use heavily depends on the field, e.g., being absent in DNA analysis. Despite more publications on automated LR systems over time, the proportion reporting Cllr remains stable. Noticeably, Cllr values lack clear patterns and depend on the area, analysis and dataset. As LR systems become more prevalent, comparing them becomes crucial. This is hampered by different studies using different datasets. We advocate using public benchmark datasets to advance the field.
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Affiliation(s)
- Stijn van Lierop
- Netherlands Forensic Institute, Laan van Ypenburg 6, The Hague, 2497GB, the Netherlands
| | - Daniel Ramos
- AUDIAS Lab, Universidad Autonoma de Madrid, Escuela Politécnica Superior, Calle Francisco Tomàs y Valiente 11, 28049, Madrid, Spain
| | - Marjan Sjerps
- Netherlands Forensic Institute, Laan van Ypenburg 6, The Hague, 2497GB, the Netherlands
- University of Amsterdam, KdVI, PO Box 94248, Amsterdam, 1090 GE, the Netherlands
| | - Rolf Ypma
- Netherlands Forensic Institute, Laan van Ypenburg 6, The Hague, 2497GB, the Netherlands
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3
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Lapp Z, Abel L, Mangeni J, Obala AA, O'Meara WP, Taylor SM, Markwalter CF. bistro: An R package for vector bloodmeal identification by short tandem repeat overlap. Methods Ecol Evol 2024; 15:308-316. [PMID: 38962557 PMCID: PMC11218906 DOI: 10.1111/2041-210x.14277] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Accepted: 12/03/2023] [Indexed: 07/05/2024]
Abstract
Measuring vector-human contact in a natural setting can inform precise targeting of interventions to interrupt transmission of vector-borne diseases. One approach is to directly match human DNA in vector bloodmeals to the individuals who were bitten using genotype panels of discriminative short tandem repeats (STRs). Existing methods for matching STR profiles in bloodmeals to the people bitten preclude the ability to match most incomplete profiles and multi-source bloodmeals to bitten individuals.We developed bistro, an R package that implements 3 preexisting STR matching methods as well as the package's namesake, bistro, a new algorithm described here. bistro employs forensic analysis methods to calculate likelihood ratios and match human STR profiles in bloodmeals to people using a dynamic threshold. We evaluated the algorithm's accuracy and compared it to existing matching approaches using a publicly-available panel of 188 single-source and 100 multi-source samples containing DNA from 50 known human sources. Then we applied it to match 777 newly field-collected mosquito bloodmeals to a database of 645 people.The R package implements four STR matching algorithms in user-friendly functions with clear documentation. bistro correctly matched 99% (187/188) of profiles in single-source samples, and 62% (224/359) of profiles from multi-source samples, resulting in a sensitivity of 0.75 (vs < 0.51 for other algorithms). The specificity of bistro was 0.9998 (vs. 1 for other algorithms). Furthermore, bistro identified 79% (720/906) of all possible matches for field-derived mosquitoes, yielding 1.4x more matches than existing algorithms.bistro identifies more correct bloodmeal-human matches than existing approaches, enabling more accurate and robust analyses of vector-human contact in natural settings. The bistro R package and corresponding documentation allow for straightforward uptake of this algorithm by others.
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Affiliation(s)
- Zena Lapp
- Duke Global Health Institute, Duke University, Durham, NC, USA
| | - Lucy Abel
- Academic Model Providing Access to Healthcare (AMPATH), Eldoret, Kenya
| | - Judith Mangeni
- Department of Epidemiology and Medical Statistics, School of Public Health, Moi University, Eldoret, Kenya
| | - Andrew A Obala
- School of Medicine, College of Health Sciences, Moi University, Eldoret, Kenya
| | - Wendy P O'Meara
- Duke Global Health Institute, Duke University, Durham, NC, USA
- Division of Infectious Diseases, School of Medicine, Duke University, Durham, NC, USA
| | - Steve M Taylor
- Duke Global Health Institute, Duke University, Durham, NC, USA
- Division of Infectious Diseases, School of Medicine, Duke University, Durham, NC, USA
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Lapp Z, Abel L, Mangeni J, Obala AA, O'Meara W, Taylor SM, Markwalter CF. bistro: An R package for vector bloodmeal identification by short tandem repeat overlap. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.09.14.23295566. [PMID: 37745593 PMCID: PMC10516083 DOI: 10.1101/2023.09.14.23295566] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
1. Measuring vector-human contact in a natural setting can inform precise targeting of interventions to interrupt transmission of vector-borne diseases. One approach is to directly match human DNA in vector bloodmeals to the individuals who were bitten using genotype panels of discriminative short tandem repeats (STRs). Existing methods for matching STR profiles in bloodmeals to the people bitten preclude the ability to match most incomplete profiles and multi-source bloodmeals to bitten individuals. 2. We developed bistro, an R package that implements 3 preexisting STR matching methods as well as the package's namesake, bistro, a new algorithm described here. bistro employs forensic analysis methods to calculate likelihood ratios and match human STR profiles in bloodmeals to people using a dynamic threshold. We evaluated the algorithm's accuracy and compared it to existing matching approaches using a publicly-available panel of 188 single-source and 100 multi-source samples containing DNA from 50 known human sources. Then we applied it to match 777 newly field-collected mosquito bloodmeals to a database of 645 people. 3. The R package implements four STR matching algorithms in user-friendly functions with clear documentation. bistro correctly matched 99% (184/185) of profiles in single-source samples, and 63% (225/359) of profiles from multi-source samples, resulting in a sensitivity of 0.75 (vs < 0.51 for other algorithms). The specificity of bistro was 0.9998 (vs. 1 for other algorithms). Furthermore, bistro identified 80% (729/909) of all possible matches for field-derived mosquitoes, yielding 1.4x more matches than existing algorithms. 4. bistro identifies more correct bloodmeal-human matches than existing approaches, enabling more accurate and robust analyses of vector-human contact in natural settings. The bistro R package and corresponding documentation allow for straightforward uptake of this algorithm by others.
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Affiliation(s)
- Zena Lapp
- Duke Global Health Institute, Duke University, Durham, NC, USA
| | - Lucy Abel
- Academic Model Providing Access to Healthcare (AMPATH), Eldoret, Kenya
| | - Judith Mangeni
- Department of Epidemiology and Medical Statistics, School of Public Health, Moi University, Eldoret, Kenya
| | - Andrew A Obala
- School of Medicine, College of Health Sciences, Moi University, Eldoret, Kenya
| | - Wendy O'Meara
- Duke Global Health Institute, Duke University, Durham, NC, USA
- Division of Infectious Diseases, School of Medicine, Duke University, Durham, NC, USA
| | - Steve M Taylor
- Duke Global Health Institute, Duke University, Durham, NC, USA
- Division of Infectious Diseases, School of Medicine, Duke University, Durham, NC, USA
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Kruijver M, Kelly H, Taylor D, Buckleton J. Addressing uncertain assumptions in DNA evidence evaluation. Forensic Sci Int Genet 2023; 66:102913. [PMID: 37453205 DOI: 10.1016/j.fsigen.2023.102913] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 07/03/2023] [Accepted: 07/10/2023] [Indexed: 07/18/2023]
Abstract
Evidential value of DNA mixtures is typically expressed by a likelihood ratio. However, selecting appropriate propositions can be contentious, because assumptions may need to be made around, for example, the contribution of a complainant's profile, or relatedness between contributors. A choice made one way or another disregards any uncertainty that may be present about such an assumption. To address this, a complex proposition that considers multiple sub-propositions with different assumptions may be more appropriate. While the use of complex propositions has been advocated in the literature, the uptake in casework has been limited. We provide a mathematical framework for evaluating DNA evidence given complex propositions and discuss its implementation in the DBLR™ software. The software simultaneously handles multiple mixed samples, reference profiles and relationships as described by a pedigree, which unlocks a variety of applications. We provide several examples to illustrate how complex propositions can efficiently evaluate DNA evidence. The addition of this feature to DBLR™ provides a tool to approach the long-accepted, but often impractical suggestion that propositions should be exhaustive within a case context.
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Affiliation(s)
- Maarten Kruijver
- Institute of Environmental Science and Research Limited, Private Bag 92021, Auckland 1142 New Zealand.
| | - Hannah Kelly
- Institute of Environmental Science and Research Limited, Private Bag 92021, Auckland 1142 New Zealand
| | - Duncan Taylor
- Forensic Science SA, GPO Box 2790, Adelaide, SA 5001, Australia; College of Science and Engineering, Flinders University, GPO Box 2100, Adelaide, SA 5001, Australia
| | - John Buckleton
- Institute of Environmental Science and Research Limited, Private Bag 92021, Auckland 1142 New Zealand; University of Auckland, Department of Statistics, Private Bag 92019, Auckland 1142, New Zealand
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Hoogenboom J, Sijen T, Benschop C. ProbRank: An efficient DNA database search method for complex mixtures per a quantitative likelihood ratio model. Forensic Sci Int Genet 2023; 65:102884. [PMID: 37150077 DOI: 10.1016/j.fsigen.2023.102884] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 04/04/2023] [Accepted: 04/27/2023] [Indexed: 05/09/2023]
Abstract
Searching a DNA Database with a DNA profile from an evidentiary trace can provide investigative leads in a forensic case. Various searching approaches exist such as conventional methods based on matching alleles or more advanced methods computing likelihood ratios (LR) while considering drop-in and drop-out. Here we examine the potential of using a quantitative LR model (EuroForMix model incorporated in ProbRank method) that takes peak heights into account in comparison to a qualitative LR model (LRmix model implemented in SmartRank method). Both methods present DNA database candidates in order of decreasing LR. Especially regarding minor contributors in complex mixtures, the method using the quantitative model outperforms the method using the qualitative model in terms of sensitivity and specificity as more true donors and less adventitious matches are retrieved. ProbRank is to be implemented in DNAStatistX and is sufficiently fast for daily use.
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Affiliation(s)
- Jerry Hoogenboom
- Division of Biological Traces, Netherlands Forensic Institute, The Hague, the Netherlands.
| | - Titia Sijen
- Division of Biological Traces, Netherlands Forensic Institute, The Hague, the Netherlands; Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, the Netherlands
| | - Corina Benschop
- Division of Biological Traces, Netherlands Forensic Institute, The Hague, the Netherlands
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Taylor D, Abarno D. Using big data from probabilistic genotyping to solve crime. Forensic Sci Int Genet 2021; 57:102631. [PMID: 34861631 DOI: 10.1016/j.fsigen.2021.102631] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 10/02/2021] [Accepted: 11/06/2021] [Indexed: 11/04/2022]
Abstract
Forensic Science South Australia (FSSA) has been using STRmix™ software to deconvolute all reported DNA mixtures since 2012. Almost a decade of deconvolutions had led to a substantial repository of analysed profile data that can be interrogated to observe trends in case type, location or occurrence. In addition, deconvolutions can be compared in order to identify common DNA donors and reveal new intelligence information in cases where DNA profiling has previously provided no investigative information. As a proof of concept all samples deconvoluted as part of criminal casework (suspect or no-suspect) were interrogated and compared to each other using the mixture-to-mixture comparison feature in STRmix™. Within the Adelaide region there were 32 groups of cases that had evidence samples linked by a common DNA donor with LR > 1 million which was in addition to direct links and mixture searching links identified previously. These groups of cases can then be interrogated to reveal additional information to inform Police intelligence gathering. Our paper reports on the findings of this proof-of-concept study.
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Affiliation(s)
- Duncan Taylor
- School of Biological Sciences, Flinders University, GPO Box 2100, Adelaide, SA 5001, Australia; Forensic Science SA, PO Box 2790, Adelaide, SA 5000, Australia.
| | - Damien Abarno
- Forensic Science SA, PO Box 2790, Adelaide, SA 5000, Australia
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Gill P, Benschop C, Buckleton J, Bleka Ø, Taylor D. A Review of Probabilistic Genotyping Systems: EuroForMix, DNAStatistX and STRmix™. Genes (Basel) 2021; 12:1559. [PMID: 34680954 PMCID: PMC8535381 DOI: 10.3390/genes12101559] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Revised: 09/24/2021] [Accepted: 09/28/2021] [Indexed: 11/24/2022] Open
Abstract
Probabilistic genotyping has become widespread. EuroForMix and DNAStatistX are both based upon maximum likelihood estimation using a γ model, whereas STRmix™ is a Bayesian approach that specifies prior distributions on the unknown model parameters. A general overview is provided of the historical development of probabilistic genotyping. Some general principles of interpretation are described, including: the application to investigative vs. evaluative reporting; detection of contamination events; inter and intra laboratory studies; numbers of contributors; proposition setting and validation of software and its performance. This is followed by details of the evolution, utility, practice and adoption of the software discussed.
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Affiliation(s)
- Peter Gill
- Forensic Genetics Research Group, Department of Forensic Sciences, Oslo University Hospital, 0372 Oslo, Norway;
- Department of Forensic Medicine, Institute of Clinical Medicine, University of Oslo, 0315 Oslo, Norway
| | - Corina Benschop
- Division of Biological Traces, Netherlands Forensic Institute, P.O. Box 24044, 2490 AA The Hague, The Netherlands;
| | - John Buckleton
- Department of Statistics, University of Auckland, Private Bag 92019, Auckland 1142, New Zealand;
- Institute of Environmental Science and Research Limited, Private Bag 92021, Auckland 1142, New Zealand
| | - Øyvind Bleka
- Forensic Genetics Research Group, Department of Forensic Sciences, Oslo University Hospital, 0372 Oslo, Norway;
| | - Duncan Taylor
- Forensic Science SA, GPO Box 2790, Adelaide, SA 5001, Australia;
- School of Biological Sciences, Flinders University, GPO Box 2100, Adelaide, SA 5001, Australia
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Statistefix 4.0: A novel probabilistic software tool. Forensic Sci Int Genet 2021; 55:102570. [PMID: 34474323 DOI: 10.1016/j.fsigen.2021.102570] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Revised: 08/09/2021] [Accepted: 08/09/2021] [Indexed: 01/09/2023]
Abstract
Latest innovations indicate that continuous tools are promising DNA trace assessment methods. In this study, we present the continuous software solution Statistefix 4.0. The software supports DNA experts in deducing DNA profiles for database queries and can help to preselect DNA samples suitable for further processing using advanced probabilistic search engines. The novel tool weights genotype contributions and deduces major contributors from high- and low-quality DNA traces. Peak height, degradation, stutter as well as allelic drop-in/-out events are incorporated in the statistical model. We analyzed reference and casework samples as well as artificially generated mixture samples for software evaluation. The tool offers the completely automated assessment of reference and mixture samples. Deconvolution outcomes of mixtures are compared with EuroForMix, GenoProof Mixture 3 and STRmix™. Data show that Statistefix 4.0 is as successful as analogously tested and implemented software. Deduced DNA profiles from casework samples highlight the potential benefit in routine casework. Statistefix 4.0 is freely available, works with replicates of different autosomal kits and enables bulk sample processing. This inter-laboratory study includes a variety of sample types and indicates a timesaving, robust and easily implemented software that supports DNA analysts in evaluating DNA traces.
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The DNAxs software suite: A three-year retrospective study on the development, architecture, testing and implementation in forensic casework. FORENSIC SCIENCE INTERNATIONAL: REPORTS 2021. [DOI: 10.1016/j.fsir.2021.100212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022] Open
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LUS+: Extension of the LUS designator concept to differentiate most sequence alleles for 27 STR loci. FORENSIC SCIENCE INTERNATIONAL: REPORTS 2020. [DOI: 10.1016/j.fsir.2020.100059] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
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An examination of STR nomenclatures, filters and models for MPS mixture interpretation. Forensic Sci Int Genet 2020; 48:102319. [DOI: 10.1016/j.fsigen.2020.102319] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2020] [Revised: 05/19/2020] [Accepted: 06/01/2020] [Indexed: 11/20/2022]
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Bleka Ø, Just R, Le J, Gill P. Automation of high volume MPS mixture interpretation using CaseSolver. FORENSIC SCIENCE INTERNATIONAL GENETICS SUPPLEMENT SERIES 2019. [DOI: 10.1016/j.fsigss.2019.09.006] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Prieto L, Gill P, Bleka Ø. How to avoid driving DNA caseworkers crazy: CaseSolver, an expert system to investigate complex crime scenes. FORENSIC SCIENCE INTERNATIONAL GENETICS SUPPLEMENT SERIES 2019. [DOI: 10.1016/j.fsigss.2019.09.036] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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DNAxs/DNAStatistX: Development and validation of a software suite for the data management and probabilistic interpretation of DNA profiles. Forensic Sci Int Genet 2019; 42:81-89. [DOI: 10.1016/j.fsigen.2019.06.015] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2019] [Revised: 06/19/2019] [Accepted: 06/20/2019] [Indexed: 01/08/2023]
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