Probabilistic expert systems for handling artifacts in complex DNA mixtures.
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.
Cowell
R G
RG
Faculty of Actuarial Science and Insurance, Cass Business School, 106 Bunhill Row, London EC1Y 8TZ, UK. rgc@city.ac.uk
Lauritzen
S L
SL
Mortera
J
J
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
D004247:DNA