Proceedings of the 25th international conference on Machine learning
| Hi-index | 3.84 |
Motivation: The problem of identifying victims in a mass disaster using DNA fingerprints involves a scale of computation that requires efficient and accurate algorithms. In a typical scenario there are hundreds of samples taken from remains that must be matched to the pedigrees of the alleged victim’s surviving relatives. Moreover the samples are often degraded due to heat and exposure. To develop a competent method for this type of forensic inference problem, the complicated quality issues of DNA typing need to be handled appropriately, the matches between every sample and every family must be considered, and the confidence of matches need to be provided. Results: We present a unified probabilistic framework that efficiently clusters samples, conservatively eliminates implausible sample-pedigree pairings, and handles both degraded samples (missing values) and experimental errors in producing and/or reading a genotype. We present a method that confidently exclude forensically unambiguous sample-family matches from the large hypothesis space of candidate matches, based on posterior probabilistic inference. Due to the high confidentiality of disaster DNA data, simulation experiments are commonly performed and used here for validation. Our framework is shown to be robust to these errors at levels typical in real applications. Furthermore, the flexibility in the probabilistic models makes it possible to extend this framework to include other biological factors such as interdependent markers, mitochondrial sequences, and blood type. Availability: The software and data sets are available from the authors upon request. Contact: epxing@cs.cmu.edu