RINQ: Reference-based Indexing for Network Queries

We consider the problem of similarity queries in biological network databases. Given a database of networks, similarity query returns all the database networks whose similarity (i.e., alignment score) to a given query network is at least a specified similarity cutoff value. Alignment of two networks is a very costly operation, which makes exhaustive comparison of all the database networks with a query impractical. To tackle this problem, we develop a novel indexing method, named RINQ (Reference-based Indexing for Biological Network Queries). Our method uses a set of reference networks to eliminate a large portion of the database quickly for each query. A reference network is a small biological network. We precompute and store the alignments of all the references with all the database networks. When our database is queried, we align the query network with all the reference networks. Using these alignments, we calculate a lower bound and an approximate upper bound to the alignment scor...

• Type: Journal
• Year: 2011
• Where: BIOINFORMATICS
• Authors: Günhan Gülsoy, Tamer Kahveci
• Keywords: Alignment Score, BIOINFORMATICS 2011, Biometrics, Database Networks, Similarity Queries