The paradigm of interleaving (i.e. blending) visual subgraph query formulation and processing by exploiting the latency offered by the GUI brings in several potential benefits such as superior system response time (SRT) and opportunities to enhance usability of graph databases. Recent efforts at implementing this paradigm are focused on subgraph isomorphism-based queries, which are often restrictive in many real-world graph applications. In this demonstration, we present a novel system called BOOMER to realize this paradigm on more generic but complex bounded 1-1 p-homomorphic(BPH) queries on large networks. Intuitively, a BPH query maps an edge of the query to bounded paths in the data graph. We demonstrate various innovative features of BOOMER, its flexibility, and its promising performance.

Visual graph query interfaces (a.k.a GUI) make it easy for non-expert users to query graphs. Recent research has laid out and implemented a vision of a novel subgraph query processing paradigm where the latency offered by the GUI is exploited to blend visual query construction and processing by generating and refining candidate result matches iteratively during query formulation. This paradigm brings in several potential benefits such as superior system response time (srt) and opportunities to enhance usability of graph databases. However, these early efforts focused on subgraph isomorphism-based graph queries where blending is performed by iterative edge-to-edge mapping. In this paper, we explore how this vision can be realized for more generic but complex 1-1 p-homomorphic p-hom) queries introduced by Fan et al. A 1-1 p-hom query maps an edge of the query to paths in the data graph. We present a novel framework called BOOMER for blending bounded 1-1 p-hom (bph ) queries, a variant of 1-1 p-hom where the length of the path is bounded instead of arbitrary length. Our framework is based on a novel online , adaptive indexing scheme called cap index. We present two strategies for CAP index construction, immediate and deferment-based, and show how they can be utilized to facilitate judicious interleaving of visual bph query formulation and query processing. BOOMER is also amenable to modifications to a bph query during visual formulation. Experiments on real-world datasets demonstrate both efficiency and effectiveness of Boomer for realizing the visual querying paradigm on an important type of graph query.

BACKGROUND: Since traditional drug research and development is often time-consuming and high-risk, there is an increasing interest in establishing new medical indications for approved drugs, referred to as drug repositioning, which provides a relatively low-cost and high-efficiency approach for drug discovery. With the explosive growth of large-scale biochemical and phenotypic data, drug repositioning holds great potential for precision medicine in the post-genomic era. It is urgent to develop rational and systematic approaches to predict new indications for approved drugs on a large scale. RESULTS: In this paper, we propose the two-pass random walks with restart on a heterogenous network, TP-NRWRH for short, to predict new indications for approved drugs. Rather than random walk on bipartite network, we integrated the drug-drug similarity network, disease-disease similarity network and known drug-disease association network into one heterogenous network, on which the two-pass random walks with restart is implemented. We have conducted performance evaluation on two datasets of drug-disease associations, and the results show that our method has higher performance than six existing methods. A case study on the Alzheimer's disease showed that nine of top 10 predicted drugs have been approved or investigational for neurodegenerative diseases. The experimental results show that our method achieves state-of-the-art performance in predicting new indications for approved drugs. CONCLUSIONS: We proposed a two-pass random walk with restart on the drug-disease heterogeneous network, referred to as TP-NRWRH, to predict new indications for approved drugs. Performance evaluation on two independent datasets showed that TP-NRWRH achieved higher performance than six existing methods on 10-fold cross validations. The case study on the Alzheimer's disease showed that nine of top 10 predicted drugs have been approved or are investigational for neurodegenerative diseases. The results show that our method achieves state-of-the-art performance in predicting new indications for approved drugs.