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Issue No.01 - January/February (2012 vol.9)
pp: 113-122
D. R. Paoletti , Dept. of Comput. Sci., Pennsylvania State Univ. Beaver, Monaca, PA, USA
D. E. Krane , Biol. Sci. Dept., Wright State Univ., Dayton, OH, USA
T. E. Doom , Dept. of Comput. Sci. & Eng., Wright State Univ., Dayton, OH, USA
M. L. Raymer , Dept. of Comput. Sci. & Eng., Wright State Univ., Dayton, OH, USA
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.
Probabilistic logic, DNA, Bioinformatics, Computational complexity, Humans, Computational biology, Biological cells,bioinformatics., DNA, mixture, probabilistic computation, optimization
D. R. Paoletti, D. E. Krane, T. E. Doom, M. L. Raymer, "Inferring the Number of Contributors to Mixed DNA Profiles", IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.9, no. 1, pp. 113-122, January/February 2012, doi:10.1109/TCBB.2011.76
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