Object-oriented Bayesian networks for paternity cases with allelic dependencies

Hepler, Amanda B.; Weir, Bruce S.

doi:10.1016/j.fsigen.2007.12.003

Cited by 19 publications

(7 citation statements)

References 24 publications

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“…Its use has been seen to be gathering momentum over the past few years [28][29][30][31] in for example DNA profiling 32 , individualisation 33 , bioforensics 34 and forensic entomology 35 . This approach has also been applied to the forensic autopsy 36 which, whilst limited to prediction of cause of death from war victims, does illustrate the potential for an expert system to be used as a viable probabilistic tool for cases if appropriate information pertaining to the case was added to the system.…”

Section: Bayesian Statisticsmentioning

confidence: 99%

Post-mortem toxicology: A pilot study to evaluate the use of a Bayesian network to assess the likelihood of fatality

Langford

Bolton

Carlin

et al. 2015

Journal of Forensic and Legal Medicine

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Northumbria University has developed Northumbria Research Link (NRL) to enable users to access the University's research output. Copyright © and moral rights for items on NRL are retained by the individual author(s) and/or other copyright owners. Single copies of full items can be reproduced, displayed or performed, and given to third parties in any format or medium for personal research or study, educational, or not-for-profit purposes without prior permission or charge, provided the authors, title and full bibliographic details are given, as well as a hyperlink and/or URL to the original metadata page. The content must not be changed in any way. Full items must not be sold commercially in any format or medium without formal permission of the copyright holder. The full policy is available online: http://nrl.northumbria.ac.uk/policies.html This document may differ from the final, published version of the research and has been made available online in accordance with publisher policies. To read and/or cite from the published version of the research, please visit the publisher's website (a subscription may be required.) Post-mortem Highlights• Systematic toxicological analysis should be routinely in death investigation.• Alternative approaches to using existing toxicological data should be considered.• Bayesian networks could be used in cases where drugs/drug combination are observed.• Influence of post-mortem redistribution requires further investigation. AbstractThe challenge of interpreting post-mortem drug concentrations is well documented and relies on appropriate sample collection, knowledge of case circumstances as well as reference to published tables of data, whilst taking into account the known issues of postmortem drug redistribution and tolerance. Existing published data has evolved from simple data tables to those now including sample origin and single to poly drug use, but additional information tends to be specific to those reported in individual case studies. We have developed a Bayesian network framework to assign a likelihood of fatality based on the contribution of drug concentrations whilst taking into account the pathological findings. This expert system has been tested against casework within the coronial jurisdiction of Sunderland, UK. We demonstrate in this pilot study that the Bayesian network can be used to proffer a degree of confidence in how deaths may be reported in cases when drugs are implicated. It has also highlighted the potential for deaths to be reported according to the pathological states at post-mortem when drugs have a significant contribution that may have an impact on mortality statistics. The Bayesian network could be used as complementary approach to assist in the interpretation of post-mortem drug concentrations.

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Section: Bayesian Statisticsmentioning

confidence: 99%

Post-mortem toxicology: A pilot study to evaluate the use of a Bayesian network to assess the likelihood of fatality

Langford

Bolton

Carlin

et al. 2015

Journal of Forensic and Legal Medicine

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“…These techniques are widely applicable to criminal identification, relatedness testing, database searching and trace evidence evaluation, such as that required for the analysis of mixed DNA or small-quantity DNA stains, detailed in the review by Biedermann and Taroni ( 54 ). Object-oriented Bayesian networks have been widely applied in forensic analysis ( 55 – 58 ). Notably, joint Bayesian analyses using gamma distribution were also successfully applied for identifying trace DNA samples in multiple mixed DNA samples ( 59 ).…”

Section: Mathematical Modelingmentioning

confidence: 99%

Current developments in forensic interpretation of mixed DNA samples (Review)

Cong

et al. 2014

Biomedical Reports

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A number of recent improvements have provided contemporary forensic investigations with a variety of tools to improve the analysis of mixed DNA samples in criminal investigations, producing notable improvements in the analysis of complex trace samples in cases of sexual assult and homicide. Mixed DNA contains DNA from two or more contributors, compounding DNA analysis by combining DNA from one or more major contributors with small amounts of DNA from potentially numerous minor contributors. These samples are characterized by a high probability of drop-out or drop-in combined with elevated stutter, significantly increasing analysis complexity. At some loci, minor contributor alleles may be completely obscured due to amplification bias or over-amplification, creating the illusion of additional contributors. Thus, estimating the number of contributors and separating contributor genotypes at a given locus is significantly more difficult in mixed DNA samples, requiring the application of specialized protocols that have only recently been widely commercialized and standardized. Over the last decade, the accuracy and repeatability of mixed DNA analyses available to conventional forensic laboratories has greatly advanced in terms of laboratory technology, mathematical models and biostatistical software, generating more accurate, rapid and readily available data for legal proceedings and criminal cases.

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“…Connectivity between these OOBNs is achieved through interface nodes (input nodes and/or output nodes) (Hugin, 2007;Jensen and Nielsen, 2007). It is clear that OOBNs enable a more structured, hierarchical approach to modelling and consequently the construction of complex and dynamic models (Koller and Pfeffer, 1997;Hepler and Weir, 2008).…”

Section: Object Oriented Bayesian Network (Oobn)mentioning

confidence: 99%

An Integrated Bayesian Network approach to Lyngbya majuscula bloom initiation

Johnson

Fielding

Hamilton

et al. 2010

Marine Environmental Research

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Blooms of the cyanobacteria Lyngbya majuscula have occurred for decades around the world. However, with the increase in size and frequency of these blooms, coupled with the toxicity of such algae and their increased biomass, they have become substantial environmental and health issues. It is therefore imperative to develop a better understanding of the scientific and management factors impacting on Lyngbya bloom initiation. This paper suggests an Integrated Bayesian Network (IBN) approach that facilitates the merger of the research being conducted by various parties on Lyngbya. Pivotal to this approach are two Bayesian networks modelling the management and scientific factors of bloom initiation. The research found that Bayesian Networks (BN) and specifically Object Oriented BNs (OOBN) and Dynamic OOBNs facilitate an integrated approach to modelling ecological issues of concern. The merger of multiple models which explore different aspects of the problem through an IBN approach can apply to many multi-faceted environmental problems.

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Object-oriented Bayesian networks for paternity cases with allelic dependencies

Cited by 19 publications

References 24 publications

Post-mortem toxicology: A pilot study to evaluate the use of a Bayesian network to assess the likelihood of fatality

Post-mortem toxicology: A pilot study to evaluate the use of a Bayesian network to assess the likelihood of fatality

Current developments in forensic interpretation of mixed DNA samples (Review)

An Integrated Bayesian Network approach to Lyngbya majuscula bloom initiation

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