In this paper we treat the cell-centred multigrid approach, which distinguishes itself from the classical vertex-centred multigrid by a non-nested hierarchy of grid nodes and the use of constant, problem-independent t...
In this paper we treat the cell-centred multigrid approach, which distinguishes itself from the classical vertex-centred multigrid by a non-nested hierarchy of grid nodes and the use of constant, problem-independent transfer operators even in complicated situations. We demonstrate, that the tool of Local Fourier Analysis can also be profitably applied in this setting. We consider in detail the standard transfer operators from literature and their respective polynomial and Fourier orders, paying special attention to the combination of piecewise constant interpolation and its adjoint. Furthermore, we give several numerical examples for model problems and an application from biomedical engineering.
The present paper describes two cooperative projects (AUTOBENCH and AUTO-OPT) carried out with partners in the automotive industries (AUDI, BMW, DaimlerChrysler, Karmann and Porsche), software vendors of simulation so...
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We propose a specification language ProML for protein sequences, structures, and families based on the open XML standard. The language allows for portable, system-independent, machine-parsable and human-readable repre...
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We propose a specification language ProML for protein sequences, structures, and families based on the open XML standard. The language allows for portable, system-independent, machine-parsable and human-readable representation of essential features of proteins. The language is of immediate use for several bioinformatics applications: we discuss clustering of proteins into families and the representation of the specific shared features of the respective clusters. Moreover, we use ProML for specification of data used in fold recognition bench-marks exploiting experimentally derived distance constraints.
MOTIVATION:Large scale gene expression data are often analysed by clustering genes based on gene expression data alone, though a priori knowledge in the form of biological networks is available. The use of this additi...
MOTIVATION:Large scale gene expression data are often analysed by clustering genes based on gene expression data alone, though a priori knowledge in the form of biological networks is available. The use of this additional information promises to improve exploratory analysis considerably.
RESULTS:We propose constructing a distance function which combines information from expression data and biological networks. Based on this function, we compute a joint clustering of genes and vertices of the network. This general approach is elaborated for metabolic networks. We define a graph distance function on such networks and combine it with a correlation-based distance function for gene expression measurements. A hierarchical clustering and an associated statistical measure is computed to arrive at a reasonable number of clusters. Our method is validated using expression data of the yeast diauxic shift. The resulting clusters are easily interpretable in terms of the biochemical network and the gene expression data and suggest that our method is able to automatically identify processes that are relevant under the measured conditions.
We present a comprehensive analysis of methods for improving the fold recognition rate of the threading approach to protein structure prediction by the utilization of few additional distance constraints. The distance ...
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We present a comprehensive analysis of methods for improving the fold recognition rate of the threading approach to protein structure prediction by the utilization of few additional distance constraints. The distance constraints between protein residues may be obtained by experiments such as mass spectrometry or NMR spectroscopy. We applied a post-filtering step with new scoring functions incorporating measures of constraint satisfaction to ranking lists of 123D threading alignments. The detailed analysis of the results on a small representative benchmark set show that the fold recognition rate can be improved significantly by up to 30% from about 54%-65% to 77%-84%, approaching the maximal attainable performance of 90% estimated by structural superposition alignments. This gain in performance adds about 10% to the recognition rate already achieved in our previous study with cross-link constraints only. Additional recent results on a larger benchmark set involving a confidence function for threading predictions also indicate notable improvements by our combined approach, which should be particularly valuable for rapid structure determination and validation of protein models.
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