DocumentCode :
1499934
Title :
Inferring the Number of Contributors to Mixed DNA Profiles
Author :
Paoletti, D.R. ; Krane, D.E. ; Raymer, M.L. ; Doom, T.E.
Author_Institution :
Dept. of Comput. Sci., Pennsylvania State Univ. Beaver, Monaca, PA, USA
Volume :
9
Issue :
1
fYear :
2012
Firstpage :
113
Lastpage :
122
Abstract :
Forensic samples containing DNA from two or more individuals can be difficult to interpret. Even ascertaining the number of contributors to the sample can be challenging. These uncertainties can dramatically reduce the statistical weight attached to evidentiary samples. A probabilistic mixture algorithm that takes into account not just the number and magnitude of the alleles at a locus, but also their frequency of occurrence allows the determination of likelihood ratios of different hypotheses concerning the number of contributors to a specific mixture. This probabilistic mixture algorithm can compute the probability of the alleles in a sample being present in a 2-person mixture, 3-person mixture, etc. The ratio of any two of these probabilities then constitutes a likelihood ratio pertaining to the number of contributors to such a mixture.
Keywords :
DNA; biochemistry; biology computing; mixtures; molecular biophysics; probability; statistical analysis; evidentiary samples; mixed DNA profiles; probabilistic mixture algorithm; statistical weight; Bioinformatics; Biological cells; Computational biology; Computational complexity; DNA; Humans; Probabilistic logic; DNA; bioinformatics.; mixture; optimization; probabilistic computation; Algorithms; Computational Biology; DNA; Forensic Genetics; Humans; Models, Statistical; Sequence Analysis, DNA;
fLanguage :
English
Journal_Title :
Computational Biology and Bioinformatics, IEEE/ACM Transactions on
Publisher :
ieee
ISSN :
1545-5963
Type :
jour
DOI :
10.1109/TCBB.2011.76
Filename :
5753885
Link To Document :
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