In recent studies [1-3], lots of hidden homology in DNA genome are not found by current comparative tools despite decades of research. Many scholars modeled the genome as a monotonous string, which limits and probably...
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ISBN:
(纸本)9783319190488;9783319190471
In recent studies [1-3], lots of hidden homology in DNA genome are not found by current comparative tools despite decades of research. Many scholars modeled the genome as a monotonous string, which limits and probably obstructs the discovery of some significant patterns. We propose an information-coding-based model called DNA As X (DAX) to improve the sensitivity in comparative genomic studies by integrating the principles and concepts of other disciplines including information coding theory and signal processing into genome analysis. The proposed DNA As X model uses character-analysis-free (CAF) techniques, where X is the intermediate for analysis that can be digit, code, signal, vector, tree, graph network and so on. It provides novel and comprehensive perspectives to further analyze and recognize the critical patterns hidden in DNA genomes. Comparing with traditional character-analysis-based (CAB) methods, DAX not only enriches the tools and the knowledge library of computational biology but also extends the domain from 1-D character string analysis to 2-D spatial/temporal domain. Furthermore, by applying the DAX model to the issue of exon prediction as an evaluation, we illustrate the insights behind this model. The experimental results show that the DAX methodology can improve the sensitivity in genome analysis by using the novel information-coding techniques.
In this paper we present a new approach for the acquisition and analysis of background knowledge which is used for 3D reconstruction of man-made objects - in this case buildings. Buildings can be easily represented as...
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ISBN:
(纸本)0819424870
In this paper we present a new approach for the acquisition and analysis of background knowledge which is used for 3D reconstruction of man-made objects - in this case buildings. Buildings can be easily represented as parameterized graphs from which p-subisomorphic graphs will be computed. P-graphs will be defined and an upper bound complexity estimation of the computation of p-subisomorphims will be given. In order to reduce search space we will discuss several pruning mechanisms. Background knowledge requires a classification in order to receive a probability distribution which will serve as a priori knowledge for 3D building reconstruction. Therefore, we will apply an alternative view of nearest-neighbor classification to measured knowledge in order to learn based on a complete seed and a noise model a distribution of this knowledge. An application of an extensive scene consisting of 1846 building cluster which are represented as p-graphs in order to estimate a probability distribution of corner nodes demonstrates the effectiveness of our approach. An evaluation using the information coding theory determines the information gain which is provided by the estimated distribution in comparison with no available a priori knowledge.
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