Network alignment is a computational technique to identify topological similarity of graph data by mapping link patterns. In bioinformatics, network alignment algorithms have been applied to protein-protein interaction (PPI) networks to discover evolutionarily conserved substructures at the system level. In particular, local network alignment of PPI networks searches for conserved functional components between species and predicts unknown protein complexes and signaling pathways. In this article, we present a novel approach of local network alignment by semantic mapping. While most previous methods find protein matches between species by sequence homology, our approach uses semantic similarity. Given Gene Ontology (GO) and its annotation data, we estimate functional closeness between two proteins by measuring their semantic similarity. We adopted a new semantic similarity measure, simVICD, which has the best performance for PPI validation and functional match. We tested alignment between the PPI networks of well-studied yeast protein complexes and the genome-wide PPI network of human in order to predict human protein complexes. The experimental results demonstrate that our approach has higher accuracy in protein complex prediction than graph clustering algorithms, and higher efficiency than previous network alignment algorithms.
Continuous collision detection (CCD) algorithm is a vital step in virtual reality. This paper describes an efficient self-collision detection algorithm called the minimum normal cone algorithm. The algorithm calculates the parent-normal cone in a new way, which optimizes the two test conditions for the self-collision detection: In the first test condition, we analyze and calculate the relative position between the two child-normal cones, and then a bottom-up technology is used to build the parent-normal cone. Thus the two child normal cones can be surrounded by the parent-normal cone perfectly, and the normal vectors motion range of the parent-triangular mesh can also be closely restricted; In the second test condition, we calculate the normal vectors motion range of the contour projection line, and check whether the projection line is self-colliding or not, to avoid excessive tests between segments. The algorithm has been implemented and tested on two classical models, cloth-ball and N-body model respectively. Experimental results demonstrate that our algorithm can decrease the number of elementary tests by two orders of magnitudes, and significantly improve the performance of the overall CCD algorithm.
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