Network querying algorithms provide computational means to identify conserved network modules in large-scale biological networks that are similar to known functional modules, such as pathways or molecular complexes. Two main challenges for network querying algorithms are the high computational complexity of detecting potential isomorphisms between graphs and ensuring the biological significance of the query results. In this work, we propose a novel network querying algorithm that can enhance the biological significance of the query results through the use of a context-sensitive random walk (CSRW) model. In order to identify conserved subnetwork regions in the target network that are similar to a given query network, the algorithm estimates the node correspondence between the query and the target networks based on the CSRW model. Inspired by the pair hidden Markov model (pair-HMM) that has been widely used in comparative sequence analysis, the CSRW model can effectively capture the similarities between graphs by explicitly accounting for potentially inserted and deleted network nodes. Based on the estimated CSRW node correspondence scores, our algorithm first identifies high-scoring regions (referred to as the seed networks) in the target network that bear considerable similarity with (part of) the query. The seed networks are further extended by maximally minimizing the network conductance, which can effectively identify nearby nodes (e.g., proteins) that may share similar functions with other nodes in the current seed. Finally, the query result is pruned by removing irrelevant nodes based on an extension reward score, thereby improving the consistency of the final query result. Through extensive numerical simulations based on 938 real biological complexes, we show that our proposed algorithm yields accurate query results with enhanced biological significance.


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