title: Probabilistic expert systems for handling artifacts in complex DNA mixtures.
abstract: This paper presents a coherent probabilistic framework for taking account of allelic dropout, stutter bands and silent alleles when interpreting STR DNA profiles from a mixture sample using peak size information arising from a PCR analysis. This information can be exploited for evaluating the evidential strength for a hypothesis that DNA from a particular person is present in the mixture. It extends an earlier Bayesian network approach that ignored such artifacts. We illustrate the use of the extended network on a published casework example.
authors.affiliation: Faculty of Actuarial Science and Insurance, Cass Business School, 106 Bunhill Row, London EC1Y 8TZ, UK. rgc@city.ac.uk
authors.forename: R G
authors.initials: RG
authors.lastname: Cowell
authors.forename: S L
authors.initials: SL
authors.lastname: Lauritzen
authors.forename: J
authors.initials: J
authors.lastname: Mortera
mesh_terms: D000483:Alleles; D016477:Artifacts; D001499:Bayes Theorem; D004247:DNA / Q000235:genetics*; D005103:Expert Systems; D018895:Microsatellite Repeats / Q000235:genetics*; D016133:Polymerase Chain Reaction / Q000379:methods; D011336:Probability
chemical_list: D004247:DNA