__CNetQ__: Network querying tool based on conditional random fields
* Last updated: June 22, 2016


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 can efficiently discover the similar subnetworks among different species. However, many existing methods only partially solve this problem. A novel approach for network querying problem based on conditional random fields (CRF) model is proposed, 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.

* Qiang Huang, Ling-Yun Wu, Xiang-Sun Zhang. [An Efficient Network Querying Method Based on Conditional Random Fields|http://bioinformatics.oxfordjournals.org/content/27/22/3173], Bioinformatics, 27(22): 3173-3178, 2011.


!!R package
The network querying method has been implemented as function net_query in [R|http://www.r-project.org] package Corbi, which can be found at:
* [Corbi]

!!Web service
The online version of CNetQ is available at
* [http://app.aporc.org/CNetQ/]

!!Matlab code and data
The Matlab code and data used in the paper published in Bioinformatics (2011):
* [data.7z|CNetQ/data.7z]
* [code.7z|CNetQ/code.7z]
__Note: The Matlab code is provided here only for record and test purposes, and not recommended for productive use since the code is not fully tuned and optimized. The users are suggested to use the R package provided above since it is more efficient in terms of both running time and memory usage.__

Category: [Supplementary] [Software]