http://dl.gi.de/handle/20.500.12116/21204 2026-07-22T20:35:16Z http://dl.gi.de/handle/20.500.12116/21224 Registration to a neuroanatomical reference atlas - identifying glomeruli in optical recordings of the honeybee brain Strauch, Martin; Galizia, C. Giovanni Beyer, Andreas; Schroeder, Michael An odorant stimulus given to a bee elicits a characteristic combinatorial pattern of activity in neuronal units called glomeruli. These patterns can be measured by optical imaging, however detecting and identifying the glomeruli is a laborious task and prone to errors. Here, we present an image analysis pipeline for the automatic detection and identification of glomeruli. It involves Independent Component Analysis (ICA) to detect glomeruli in CCD camera data, a filtering step to exclude non- glomerulus objects and a graph-matching approach to find the best projection of the observed brain region onto a reference atlas. We evaluate our method against a manual glomerulus identification performed by a human expert and show that we achieve reliable results. Employing our method, we are now able to screen multiple recordings with the same accuracy, yielding a homogeneous collection of glomerulus identity mappings. These will subsequently be used to extract activity patterns that can be compared between individuals. 2008-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/21220 Evolutionary Construction of Multiple Graph Alignments for the Structural Analysis of Biomolecules Fober, Thomas; Hüllermeier, Eyke; Mernberger, Marco Beyer, Andreas; Schroeder, Michael The concept of multiple graph alignment has recently been introduced as a novel method for the structural analysis of biomolecules. Using inexact, approximate graph-matching techniques, this method enables the robust identification of approximately conserved patterns in biologically related structures. In particular, multiple graph alignments enable the characterization of functional protein families independent of sequence or fold homology. This paper first recalls the concept of multiple graph alignment and then addresses the problem of computing optimal alignments from an algorithmic point of view. In this regard, a method from the field of evolutionary algorithms is proposed and empirically compared to a hitherto existing greedy strategy. Empirically, it is shown that the former yields significantly better results than the latter, albeit at the cost of an increased runtime. 2008-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/21223 Protein Structure Alignment through a Contact Topology Profile using SABERTOOTH Teichert, F.; Bastolla, U.; Porto, M. Beyer, Andreas; Schroeder, Michael The contact vector (CV) of a protein structure is one of the simplest and most condensed descriptions of protein structure available. It lists the number of con- tacts each amino acid has with the surrounding structure and has frequently been used e.g. to derive approximative folding energies in protein folding analysis. The CV, however, is a lossy structure representation, as it does not contain sufficient information to allow for the reconstruction of the full protein structure it was derived from. The loss of information leads to a degeneracy in the sense that a single contact vector is compatible with many different contact matrices, but it has been shown that this degeneracy is nearly fully compensated by the physical constraints protein structure is subject to. We recently developed the alignment framework ‘SABERTOOTH’ that is able to generically align connectivity related vectorial structure profiles to compute protein alignments. Here we show that also the CV allows for state-of-the-art alignment quality, just like the elaborated ‘Effective Connectivity’ profile (EC) that SABERTOOTH currently uses. This simplification leeds to a very simple and elegant approach to structure alignment, which accelerates and generalizes the algorithm we previously proposed. Furthermore, we conclude from our work that the CV in itself is a useful structure description if its collective properties are called for. 2008-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/21221 A Propagation-based Algorithm for Inferring Gene-Disease Associations Vanunu, Oron; Sharan, Roded Beyer, Andreas; Schroeder, Michael A fundamental challenge in human health is the identification of disease- causing genes. Recently, several studies have tackled this challenge via a two-step approach: first, a linkage interval is inferred from population studies; second, a computational approach is used to prioritize genes within this interval. State-of-the-art methods for the latter task are based on the observation that genes causing the same or similar diseases tend to lie close to one another in a network of protein-protein or functional interactions. However, most of these approaches use only local network information in the inference process. Here we provide a global, network-based method for prioritizing disease genes. The method is based on formulating constraints on the prioritization function that relate to its smoothness over the network and usage of prior information. A propagation-based method is used to compute a function satisfying the constraints. We test our method on gene-disease association data in a cross-validation setting, and compare it to extant prioritization approaches. We show that our method provides the best overall performance, ranking the true causal gene first for 29% of the 1,369 diseases with a known gene in the OMIM knowledgebase. 2008-01-01T00:00:00Z