DNA samples are a part of the collected physical evidence during the comtemporary crime scene investigation procedure. After processing the samples, a laboratory obtains short tandem repeat electropherograms. In case of mixed DNA profiles, i.e., profiles that contain DNA material from more than one contributor, the laboratory needs to estimate the test statistic (likelihood ratio) that could provide evidence, either inculpatory or exculpatory, against the person of interest. This is automated with probabilistic genotyping (PG) software with (fully-)continuous models: the ones that consider the heights of the observed peaks.
In this thesis, we provide understanding of the modern PG methods. We then show how to improve measurable indicators of the algorithm performance, such as precision and inference runtime, that directly correspond to the efficiency and efficacy of work performed in a lab. With quicker algorithms the forensics laboratories can process more samples and provide more comprehensive results by reanalysing the mixtures with different hypotheses and hyperparameterisations. With more precise algorithms, there will be a grater confidence in their results. The precision of the solution would ameliorate the admissibility of the provided evidence and reliability of the results. We achieve improvements over the state-of-the-art by utilising probabilistic programming and modern Bayesian inference methods. We describe a differentiable (and hence continuous) continuous model that can be used with different estimators from both the sampling and variational families of techniques.
Finally, as the different PG products output different likelihood ratios, we provide explanation of some of the factors causing this behaviour. This is of high importance because if two solutions are used for the same crime case, the difference must be understood. Otherwise, because of lack of consensus, the results would cause confusion or, in the worst case, would not be admitted by the court.
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:92118 |
| Date | 19 June 2024 |
| Creators | Susik, Mateusz |
| Contributors | Andres, Bjoern, Sbalzarini, Ivo, Technische Universität Dresden |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, doc-type:doctoralThesis, info:eu-repo/semantics/doctoralThesis, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | 10.1016/j.fsigen.2022.102744, 10.1016/j.fsigen.2023.102840, 10.1016/j.fsigen.2023.102890, 10.48550/arXiv.2307.00015 |
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