Abstract
Prediction of molecular interaction networks from large-scale datasets in genomics and other omics experiments is an important task in terms of both developing bioinformatics methods and solving biological problems. We have applied a kernel-based network inference method for extracting functionally related genes to the response of nitrogen deprivation in cyanobacteria Anabaena PCC7120 integrating three heterogeneous datasets: microarray, phylogenetic profiles, and gene orders on the chromosome. We obtained 1348 predicted genes that are somehow related to known genes in KEGG PATHWAY. While this dataset contained previously known genes related to the nitrogen deprivation response, it also contained unknown genes. Moreover, we attempted to select any relevant genes using the constraints of Pfam domains and NtcA binding sites. We found candidates of nitrogen metabolism-related genes, which are depicted as extensions of existing KEGG PATHWAYs. We showed promising results suggesting that our approach will be helpful in designing experiments in the post-genome era.
