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Title: Forensic Signature Detection of Yersinia Pestis Culturing Practices Across Institutions Using a Bayesian Network

Abstract

The field of bioforensics is focused on the analysis of evidence from a biocrime. Existing laboratory analyses can identify the specific strain of an organism in the evidence, as well signatures of the specific culture batch of organisms, such as low-frequency contaminants or indicators of growth and processing methods. To link these disparate types of physical data to potential suspects, investigators may need to identify institutions or individuals whose access to strains and culturing practices match those identified from the evidence. In this work we present a Bayesian statistical network to fuse different types of analytical measurements that predict the production environment of a Yersinia pestis sample under investigation with automated test processing of scientific publications to identify institutions with a history of growing Y. pestis under similar conditions. Furthermore, the textual and experimental signatures were evaluated recursively to determine the overall sensitivity of the network across all levels of false positives. We illustrate that institutions associated with several specific culturing practices can be accurately selected based on the experimental signature from only a few analytical measurements. These findings demonstrate that similar Bayesian networks can be generated generically for many organisms of interest and their deployment is not prohibitive duemore » to either computational or experimental factors.« less

Authors:
; ; ; ; ; ; ;
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1132680
Report Number(s):
PNNL-SA-96751
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Journal Article
Journal Name:
Journal of Forensic Investigation, 2(1):Article No. 7
Additional Journal Information:
Journal Name: Journal of Forensic Investigation, 2(1):Article No. 7
Country of Publication:
United States
Language:
English
Subject:
Bayesian networks; bioforensics; fusion; integration; intelligence; probability

Citation Formats

Webb-Robertson, Bobbie-Jo M., Corley, Courtney D., McCue, Lee Ann, Clowers, Brian H., Dowling, Chase P., Wahl, Karen L., Wunschel, David S., and Kreuzer, Helen W. Forensic Signature Detection of Yersinia Pestis Culturing Practices Across Institutions Using a Bayesian Network. United States: N. p., 2014. Web.
Webb-Robertson, Bobbie-Jo M., Corley, Courtney D., McCue, Lee Ann, Clowers, Brian H., Dowling, Chase P., Wahl, Karen L., Wunschel, David S., & Kreuzer, Helen W. Forensic Signature Detection of Yersinia Pestis Culturing Practices Across Institutions Using a Bayesian Network. United States.
Webb-Robertson, Bobbie-Jo M., Corley, Courtney D., McCue, Lee Ann, Clowers, Brian H., Dowling, Chase P., Wahl, Karen L., Wunschel, David S., and Kreuzer, Helen W. 2014. "Forensic Signature Detection of Yersinia Pestis Culturing Practices Across Institutions Using a Bayesian Network". United States.
@article{osti_1132680,
title = {Forensic Signature Detection of Yersinia Pestis Culturing Practices Across Institutions Using a Bayesian Network},
author = {Webb-Robertson, Bobbie-Jo M. and Corley, Courtney D. and McCue, Lee Ann and Clowers, Brian H. and Dowling, Chase P. and Wahl, Karen L. and Wunschel, David S. and Kreuzer, Helen W.},
abstractNote = {The field of bioforensics is focused on the analysis of evidence from a biocrime. Existing laboratory analyses can identify the specific strain of an organism in the evidence, as well signatures of the specific culture batch of organisms, such as low-frequency contaminants or indicators of growth and processing methods. To link these disparate types of physical data to potential suspects, investigators may need to identify institutions or individuals whose access to strains and culturing practices match those identified from the evidence. In this work we present a Bayesian statistical network to fuse different types of analytical measurements that predict the production environment of a Yersinia pestis sample under investigation with automated test processing of scientific publications to identify institutions with a history of growing Y. pestis under similar conditions. Furthermore, the textual and experimental signatures were evaluated recursively to determine the overall sensitivity of the network across all levels of false positives. We illustrate that institutions associated with several specific culturing practices can be accurately selected based on the experimental signature from only a few analytical measurements. These findings demonstrate that similar Bayesian networks can be generated generically for many organisms of interest and their deployment is not prohibitive due to either computational or experimental factors.},
doi = {},
url = {https://www.osti.gov/biblio/1132680}, journal = {Journal of Forensic Investigation, 2(1):Article No. 7},
number = ,
volume = ,
place = {United States},
year = {Fri Mar 21 00:00:00 EDT 2014},
month = {Fri Mar 21 00:00:00 EDT 2014}
}