GRAPES-DD: Exploiting Decision Diagrams for Index-Driven Biomedical Databases Search

Abstract

Background: Graphs are mathematical structures widely used for expressing relationships among elements when representing biomedical and biological information. On top of these representations, several analyses are conducted. A common task is the search of one substructure, called query, within one graph, called target. The problem is referred to as one-to-one subgraph search, and it is shown to be NP-complete. However, heuristics and indexing techniques can be applied to facilitate the search. Such indexing techniques are also exploited in the context of searching in a collection of target graphs, referred to as one-to-many subgraph problem. Filter-and-verification methods that use indexing approaches provide a fast pruning of target graphs or parts of them that do not contain the query. The expensive verification phase is then performed only on the subset of promising targets. Indexing strategies extract graph features at a sufficient granularity level for performing a powerful filtering step. Features are memorized in data structures allowing an efficient access. Indexing size, querying time and filtering power are key points for the development of efficient subgraph searching solutions. Results: An existing subgraph approach, GRAPES, has been shown to have good performance in term of speed-up for both one-to-one and one-to-many cases. However, it suffers in the size of the built index. For this reason, we propose GRAPES-DD, a modified version of GRAPES in which the indexing structure has been replaced with a Decision Diagram. Decision Diagrams are a broad class of data structures widely used to encode and manipulate functions efficiently. Our experiments on real biomedical structures and synthetic graphs have confirmed our expectation showing that GRAPES-DD has substantially reduced the memory utilization with respect to GRAPES without worsening the searching time. Conclusion: The use of Decision Diagrams for searching in biomedical graphs is completely new and potentially promising thanks to their ability to encode compactly sets by exploiting their structure and regularity, and (ii) to manipulate entire sets of elements at once, instead of exploring each single element explicitly. This work shows that search strategies based on Decision Diagram makes the indexing for biochemical graphs, and not only, more scalable and affordable allowing us to potentially deal with huge and ever growing collections of biomedical structures.