The classification of protein structures is essential for their function determination in bioinformatics. It has been estimated that around 10% of all known enzymes have TIM barrel domains from the Structural Classifi...
The classification of protein structures is essential for their function determination in bioinformatics. It has been estimated that around 10% of all known enzymes have TIM barrel domains from the Structural Classification of Proteins (SCOP) database. With its high sequence variation and diverse functionalities, TIM barrel protein becomes to be an attractive target for protein engineering and for the evolution study. Hence, in this paper, an alignment approach with the best hit strategy is proposed to classify the TIM barrel protein structure in terms of superfamily and family levels in the SCOP. This work is also used to do the classification for class level in the Enzyme nomenclature (ENZYME) database. Two testing data sets, TIM40D and TIM95D, both are used to evaluate this approach. The resulting classification has an overall prediction accuracy rate of 90.3% for the superfamily level in the SCOP, 89.5% for the family level in the SCOP and 70.1% for the class level in the ENZYME. These results demonstrate that the alignment approach with the best hit strategy is a simple and viable method for the TIM barrel protein structure classification, even only has the amino acid sequences information.
Two statistical approaches for 3-D reconstruction from an image sequence are compared: the asymptotic Bayesian surface reconstruction and the Kalman filter-based depth estimation. Both techniques are recursive algorit...
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Two statistical approaches for 3-D reconstruction from an image sequence are compared: the asymptotic Bayesian surface reconstruction and the Kalman filter-based depth estimation. Both techniques are recursive algorithms where relevant information contained in previously taken images is summarized in a prior term (prior to the taking of the next image). This means that the reconstruction results are based upon information from all images but the storage and computation required do not grow dramatically. Experiments with both real images and computer generated images demonstrate that the asymptotic Bayesian approach achieves better results than the Kalman filter-based approach, largely due to better problem formulation.< >
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