CNetQ (abbreviated to Conditional Random Fields based Network Querying Method).

Motivation: A large amount of biomolecular network data for multiple species have been generated by high-throughput experimental techniques, including undirected and directed networks such as protein-protein interaction networks, gene regulatory networks and metabolic networks. There are many conserved functionally similar modules and pathways among multiple biomolecular networks in different species, therefore, it is important to analyze the similarity between the biomolecular networks. Network querying approaches aim at efficiently discovering the similar subnetworks among different species. However, many existing methods only partially solve this problem.

Results: In this paper, a novel approach for network querying problem based on conditional random fields (CRF) model is presented, which can handle both undirected and directed networks, acyclic and cyclic networks, and any number of insertions/deletions. The CRF method is fast and can query pathways in a large network in seconds using a PC. To evaluate the CRF method, extensive computational experiments are conducted on the simulated and real data, and the results are compared with the existing network querying methods. All results show that the CRF method is very useful and efficient to find the conserved functionally similar modules and pathways in multiple biomolecular networks.

Availability: R package, Matlab code and data are available at

CNetQ is implemented based on R. All the detail can be found in our paper and supplementary material.


Qiang Huang, Ling-Yun Wu, and Xiang-Sun Zhang. An Efficient Network Querying Method Based on Conditional Random Fields. Bioinformatics, 27 (22): 3173-3178, 2011. doi: 10.1093/bioinformatics/btr524.

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