Aims:
The aim of this article was to find functional (or disease-relevant) modules using gene expression data.
Background:
Biotechnological developments are leading to a rapid increase in the volume of transcriptome data and thus driving the growth of interactome data. This has made it possible to perform transcriptomic analysis by integrating interactome data. Considering that genes do not exist nor operate in isolation, and instead participate in biological networks, interactomics is equally important to expression profiles.
Objective:
We constructed a network-based method based on gene expression data in order to identify functional (or disease-relevant) modules.
Method:
We used the energy minimization with graph cuts method by integrating gene interaction networks under the assumption of the ‘guilt by association’ principle.
Result:
Our method performs well in an independent simulation experiment and has the ability to identify strongly disease-relevant modules in real experiments. Our method is able to find important functional modules associated with two subtypes of lymphoma in a lymphoma microarray dataset. Moreover, the method can identify the biological subnetworks and most of the genes associated with Duchenne muscular dystrophy.
Conclusion:
We successfully adapted the energy minimization with the graph cuts method to identify functionally important genes from genomic data by integrating gene interaction networks.
Inference of gene interaction networks using conserved subsequential patterns from multiple time course gene expression datasets
