Real-time, continuous level of detail rendering of height fields
SIGGRAPH '96 Proceedings of the 23rd annual conference on Computer graphics and interactive techniques
The multilevel finite element method for adaptive mesh optimization and visualization of volume data
VIS '97 Proceedings of the 8th conference on Visualization '97
Wavelets for computer graphics: theory and applications
Wavelets for computer graphics: theory and applications
Unconstrained Automatic Image Matching Using Multiresolutional Critical-Point Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiresolution Representation and Visualization of Volume Data
IEEE Transactions on Visualization and Computer Graphics
Interactive Visualization and Analysis for Gene Expression Data
HICSS '02 Proceedings of the 35th Annual Hawaii International Conference on System Sciences (HICSS'02)-Volume 6 - Volume 6
Geometry clipmaps: terrain rendering using nested regular grids
ACM SIGGRAPH 2004 Papers
Wavelet-based multiresolution with n√2 subdivision
Computing - Geometric modelling dagstuhl 2002
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We present a visual exploration system supporting protein analysis when using gel-free data acquisition methods. The data to be analyzed is obtained by coupling liquid chromatography (LC) with mass spectrometry (MS). LC-MS data have the properties of being nonequidistantly distributed in the time dimension (measured by LC) and being scattered in the mass-to-charge ratio dimension (measured by MS). We describe a hierarchical data representation and visualization method for large LC-MS data. Based on this visualization, we have developed a tool that supports various data analysis steps. Our visual tool provides a global understanding of the data, intuitive detection and classification of experimental errors, and extensions to LC-MS/MS, LC/LC-MS, and LC/LC-MS/MS data analysis. Due to the presence of randomly occurring rare isotopes within the same protein molecule, several intensity peaks may be detected that all refer to the same peptide. We have developed methods to unite such intensity peaks. This deisotoping step is visually documented by our system, such that misclassification can be detected intuitively. For differential protein expression analysis, we compute and visualize the differences in protein amounts between experiments. In order to compute the differential expression, the experimental data need to be registered. For registration, we perform a nonrigid warping step based on landmarks. The landmarks can be assigned automatically using protein identification methods. We evaluate our methods by comparing protein analysis with and without our interactive visualization-based exploration tool.