Michael Hanselmann

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Imaging mass spectrometry (IMS) is a promising technology which allows for detailed analysis of spatial distributions of (bio)molecules in organic samples. In many current applications, IMS relies heavily on (semi)automated exploratory data analysis procedures to decompose the data into characteristic component spectra and corresponding abundance maps,(More)
MOTIVATION Alignment of multiple liquid chromatography/mass spectrometry (LC/MS) experiments is a necessity today, which arises from the need for biological and technical repeats. Due to limits in sampling frequency and poor reproducibility of retention times, current LC systems suffer from missing observations and non-linear distortions of the retention(More)
We show on imaging mass spectrometry (IMS) data that the Random Forest classifier can be used for automated tissue classification and that it results in predictions with high sensitivities and positive predictive values, even when intersample variability is present in the data. We further demonstrate how Markov Random Fields and vector-valued median(More)
This is a preliminary experience report about designing and implementing a tableaux-based DL reasoner suitable to run on a mobile device. For now the reasoning system only offers pure TBox subsumption. Currently there is no pre-processing for efficient taxonomy computation nor ABox support. The reasoner supports the DIG standard for client communication.(More)
Members of social network platforms often choose to reveal private information, and thus sacrifice some of their privacy, in exchange for the manifold opportunities and amenities offered by such platforms. In this article, we show that the seemingly innocuous combination of knowledge of confirmed contacts between members on the one hand and their email(More)
In this paper, we introduce a new and straightforward criterion for successive insertion and deletion of training points in sparse Gaussian process regression. Our novel approach is based on an approximation of the selection technique proposed by Smola and Bartlett [1]. It is shown that the resulting selection strategies are as fast as the purely randomized(More)
Digital staining for the automated annotation of mass spectrometry imaging (MSI) data has previously been achieved using state-of-the-art classifiers such as random forests or support vector machines (SVMs). However, the training of such classifiers requires an expert to label exemplary data in advance. This process is time-consuming and hence costly,(More)
Could online social networks like Facebook be used to infer relationships between non-members? We show that the combination of relationships between members and their e-mail contacts to non-members provides enough information to deduce a substantial proportion of the relationships between non-members. Using structural features we are able to predict(More)
Mass spectrometry imaging (MSI) is an emerging, promising technology that combines mass spectrometry (MS) with microscopic imaging. This allows the simultaneous analysis of the spatial distribution of thousands of different (bio-)molecules. However, the amount of data acquired renders a direct manual analysis infeasible. This thesis introduces novel methods(More)