| Biological network, which can be used as a model of describing thefunctional associations of molecules derived from genomic data, has beenan emerging field in recovering the basic molecular processes and rules,for instance, organism growth,development, aging,diseases and so on.For the conservative feature of biological system, frequent pattern miningfrom the biological networks can be used to exclude a variety ofinterference from biological data so that the pattern with biologicalsignificance can be digged out.Due to the features of large scales, high dimensions, various typesand high-throughput noise of biological networks, traditional clusteringalgorithms of biological networks often have the high time complexity. Inthis paper, through an iterative refinement strategy ofNetworkset-Summary Graph-Candidate Networkset, we take the localtopology feature of the frequent pattern into account to design a simple, efficient and scalable mining algorithms, and eventually develop a visualapplication. We integrated the gene expression datasets about thesaccharomyces cerevisiae, and constructed20co-expression networks.Then we applied our algorithm to these co-expression networks, andperformed the Gene Ontology analysis of the result by the GOEAST. Theresults show that the genes contained in the FDVS are enriched GOterms.Aging is characterized by the progressive functional decline ofmultiple organs and tissues, eventually culminating in death. However, atthe molecular level, difference expression of genes at different stage leadsto aging. We use the gene expression of hippocampus in C57BL/6miceaged1month,6month,16month, and24month, to construct theco-expression networks. Then we applied our algorithm to theseco-expression networks, and performed the Gene ontology analysis of theresult by the DAVID. The results that some of the enriched GO terms isrelated to aging, somehow reflects that our algorithm is valuable to theresearch of difference gene expression in aging. |
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