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Traditional correlation network analysis typically involves creating a network using gene expression data and then identifying biologically relevant clusters from that network by enrichment with Gene Ontology or pathway information. When one wants to examine these networks in a dynamic way - such as between controls versus treatment or over time - a "snapshot" approach is taken by comparing network structures at each time point. The biological relevance of these structures are then reported and compared. In this research, we examine the same "snapshot" networks but focus on the enrichment of changes in structure to determine if these results give any more insight into the mechanisms behind observed phenotypes. Our main hypothesis is that more information, particularly related to potential dynamic changes, can be obtained through transition­-based analysis of biological networks. To test this hypothesis, we compare gene expression data from the mouse hippocampus at three different time points: young, middle-aged, and aged, and compare the traditional state-based approach to the dynamic transition-based enrichment approach. In this study we use a clustering approach (SPICi) designed specifically for clustering of large biological networks. The results of this study verify an inconsistency between traditional and dynamic structure identification approaches through biological enrichment. These results highlight an intriguing issue for those performing, critiquing, and using network based approaches in their research - that a black box or workflow type of approach typically used in network based research can be supplemented with a transition­-based approach to support movement from in silico to in vivo experimentation of target genes.


2015 IEEE International Conference on Bioinformatics and Biomedicine (BTBM)

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