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