Forensic DNA Phenotyping
Freitag, 14.7.17, 12:00-13:00, Raum 404, Eckerstr. 1
Forensic DNA Phenotyping (FDP) is a relatively new development in the field of forensic genetics. It aims at predicting selected so-called externally visible characteristics (EVCs) of a trace donor from their DNA as left behind at the crime scene. The best results for FDP were achieved for eye colour where the IrisPlex DNA test system was developed (Walsh et al. 2011), which includes six SNPs in six different genes, and was found to obtain relatively high levels of prediction. The second best predictable EVC after eye colour is hair colour.\n In the first part of this talk, results of a study investigating the prediction of the pigmentation phenotypes eye, hair and skin colour in a Northern German population will be presented (Caliebe et al. 2016). With this study, we aimed at answering the following research questions: (1) do existing models allow good prediction of high-quality phenotypes in a genetically similar albeit more homogeneous population? (2) Would a model specifically set up for the more homogeneous population perform notably better than existing models? (3) Can the number of markers included in existing models be reduced without compromising their predictive capability in the more homogenous population?\nIn the second part of the talk we differentiate FDP from trace donor identification problems. In the latter, it has become widely accepted in forensics that, owing to a lack of sensible priors, the evidential value of matching DNA profiles is most sensibly communicated in the form of a likelihood ratio (LR). This agreement is not in contradiction to the fact that the posterior odds (PO) would be the preferred basis for returning a verdict. A completely different situation holds for FDP. The statistical models underlying FDP typically yield PO for an individual possessing a certain EVC. This apparent discrepancy has led to confusion as to when LR or PO is the appropriate outcome of forensic DNA analysis to be communicated. We thus set out to clarify the distinction between LR and PO in the context of forensic DNA profiling and FDP from a statistical point of view (Caliebe et al. 2017). \nCaliebe, A., M. Harder, R. Schuett, M. Krawczak, A. Nebel and N. von Wurmb-Schwark, 2016. The more the merrier? How a few SNPs predict pigmentation phenotypes in the Northern German population. Eur. J. Hum. Genet. 24: 739-747.\nCaliebe, A., S. Walsh, F. Liu, M. Kayser and M. Krawczak, 2017. Likelihood ratio and posterior odds in forensic genetics: Two sides of the same coin. Forensic Sci Int Genet 28: 203-210.\nWalsh, S., F. Liu, K. N. Ballantyne, M. van Oven, O. Lao and M. Kayser, 2011. IrisPlex: a sensitive DNA tool for accurate prediction of blue and brown eye colour in the absence of ancestry information. Forensic Sci Int Genet 5: 170-180.
Time-delay reservoir computers: nonlinear stability of functional differential systems and optimal nonlinear information processing capacity. Applications to stochastic nonlinear time series forecasting.
Freitag, 14.7.17, 13:00-14:00, Raum 404, Eckerstr. 1
Reservoir computing is a recently introduced brain-inspired\nmachine learning paradigm capable of excellent performances in the processing of empirical data. We focus on a particular kind of time-delay based reservoir computers that have been physically implemented using optical and electronic systems and have shown unprecedented data processing rates. Reservoir computing is well-known for the ease of the associated training scheme but also for the problematic sensitivity of its performance to architecture parameters.\nThis talk addresses the reservoir design problem, which remains the biggest challenge in the applicability of this information processing scheme. More specifically, we use the information available regarding the optimal reservoir working regimes to construct a functional link between the reservoir parameters and its performance. This function is\nused to explore various properties of the device and to choose the optimal reservoir architecture, thus replacing the tedious and time consuming parameter scanning used so far in the literature.\n
Linking differential equation modeling to population statistics in metabolomics - insights from general population data for statistical analyses and study design of metabolome wide association analyses
Freitag, 28.7.17, 12:00-13:00, Raum 404, Eckerstr. 1
Metabolomics has developed fast in the last decade, presenting promising results, both in terms of improving the understanding of physiological and pathophysiological processes and in terms of predictive and diagnostic models aiming at personalized medicine. However, statistical modeling has been relying almost exclusively on linear models like partial least squares or ordinary least squares regression analyses, despite them being physiologically implausible in a wide range of scenarios. Here, by using data from the large general population cohort Study of Health in Pomerania (SHIP, n=4068), we show that differential equation modeling can be utilized to inform and refine statistical regression models on the population level, describing successfully important features of one-time metabolome measures. As shown, the information derived from differential equation modeling can then be used to modify and optimize several steps of metabolome wide association analyses from data sampling (e.g. which factors should be sampled or controlled) and data preparation (e.g normalization of urine data) to model specification (e.g. correct adjustment for important confounder) and data interpretation (e.g. metabolite-phenotype interactions). In conclusion, we demonstrate that metabolome data contain more information than usually extracted and that theoretical modelling via differential equations can be helpful in understanding attributes of one-time metabolomic measurements, paving the way for better applications of metabolomics in the clinical sciences.\n