SAGA stands for Substructure Index-based Approximate Graph Alignment. It is an efficient tool for approximate subgraph matching. SAGA allows users to match a query graph against a large database of graphs. At the core of SAGA is a flexible graph distance model that incorporates node approximate matching, node insertion and deletion, as well as approximate structure matching. A powerful indexing method is implemented to speed up the matching process. Some applications of SAGA include querying/comparing pathways and querying parsed biomedical literature databases to find similar documents.
Right now, we provide 2 pathway datasets that the users can query against. The datasets are described below: ** UPDATED on April 12, 2010 **
| Database | Size | Description |
| KEGG Human Pathways (downloaded on July 5, 2009) | 201 Pathways | KEGG Human Metabolic Pathways and Human Regulatory Pathways. |
| All KEGG Pathways (downloaded on July 5, 2009) | 102,570 Pathways | All pathways of the 1043 species in KEGG. |
If you have any questions or suggestions, please contact ytian [at] umich [dot] edu or jignesh [at] umich [dot] edu
Last updated Jan 11, 2008.