__CNetQ__: Network querying tool based on conditional random fields
* Last updated: October 21, 2011

!!!Introduction

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.

!!!Reference
* Qiang Huang, Ling-Yun Wu, Xiang-Sun Zhang. [An Efficient Network Querying Method Based on Conditional Random Fields|http://bioinformatics.oxfordjournals.org/content/early/2011/09/16/bioinformatics.btr524], Bioinformatics, doi: 10.1093/bioinformatics/btr524, 2011.

!!!Software

!!R package
The network querying method has been implemented as function net.query in R package Corbi, which can be found at R-Forge:
* [Corbi|https://r-forge.r-project.org/projects/corbi/]

To install this package directly within R, type:
{{{install.packages("Corbi", repos="http://R-Forge.R-project.org")}}}

!!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]

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