Subgraph Matching Algorithm Research Articles

EXPLORING A SUBGRAPH MATCHING APPROACH FOR EXTRACTING BIOLOGICAL EVENTS FROM LITERATURE

An important task in biological information extraction is to identify descriptions of biological relations and events involving genes or proteins. In this work, we propose a graph‐based approach to automatically learn rules for detecting biological events in the life science literature. The event rules are learned by identifying the key contextual dependencies from full parsing of annotated text. The detection is performed by searching for isomorphism between event rules and the dependency graphs of complete sentences. When applying our approach to the data sets of the Task 1 of the BioNLP‐ST 2009, we achieved a 40.71% F‐score in detecting biological events across nine event types. Our 56.32% precision is comparable with the state‐of‐the‐art systems. The approach may also be generalized to extract events from other domains where training data are available because it requires neither manual intervention nor external domain‐specific resources. The subgraph matching algorithm we developed is released under the new BSD license and can be downloaded from http://esmalgorithm.sourceforge.net.

Comparison of representational spaces based on structural information in the development of QSAR models for benzylamino enaminone derivatives

In this paper we study different representational spaces of molecule data sets based on 2D representation models for the building of QSAR models for the prediction of the activity of 37 benzylamino enaminone derivatives. Approximations based on classical similarity calculated from fingerprint representation of molecules and isomorphism obtained using sub-graph matching algorithms are compared to fragmentation-based approximations using partial least squares and genetic algorithms. The influence of the anchored position of a non-common moiety and the kind of substituents in the common core structure of the data set are analysed, demonstrating the anomalous behaviour of some molecules and therefore the difficulty in building prediction models. These problems are solved by considering approximate similarity models. These models tune the prediction equations based on the size of the substituent and the anchored position, by adjusting the contribution of each substituent in similarity measurements calculated between the molecule data sets.

Graphtracker: A topology projection invariant optical tracker

In this paper, we describe a new optical tracking algorithm for pose estimation of interaction devices in virtual and augmented reality. Given a 3D model of the interaction device and a number of camera images, the primary difficulty in pose reconstruction is to find the correspondence between 2D image points and 3D model points. Most previous methods solved this problem by the use of stereo correspondence. Once the correspondence problem has been solved, the pose can be estimated by determining the transformation between the 3D point cloud and the model. Our approach is based on the projective invariant topology of graph structures. The topology of a graph structure does not change under projection: in this way we solve the point correspondence problem by a subgraph matching algorithm between the detected 2D image graph and the model graph. In addition to the graph tracking algorithm, we describe a number of related topics. These include a discussion on the counting of topologically different graphs, a theoretical error analysis, and a method for automatically estimating a device model. Finally, we show and discuss experimental results for the position and orientation accuracy of the tracker.