Compression of Biological Networks using a Genetic Algorithm with Localized Merge

Abstract

Network graphs appear in a number of important biological data problems, recording information relating to protein-protein interactions, gene regulation, transcription regulation and much more. These graphs are of such a significant size that they are impossible for a human to understand. Furthermore, the ever-expanding quantity of such information means that there are storage issues. To help address these issues, it is common for applications to compress nodes to form supernodes of similarly connected components. In previous graph compression studies it was noted that such supernodes often contain points from disparate parts of the graph. This study aims to correct this flaw by only allowing merges to occur within a local neighbourhood rather than across the entire graph. This restriction was found to not only produce more meaningful compressions, but also to reduce the overall distortion created by the compression for two out of three biological networks studied.

Extracting Information from Weighted Contact Networks via Genetic Algorithms

“…For both the unweighted and weighted graphs, we count only the number of edges when determining this distance, not their weights. Previous work [2], [10] identified that in general, lower distances provided better results. The best values for distance are expected to vary from one graph to another.…”

“…We consider two fitness functions, both of which are based upon the distortion of the original graph when it undergoes the process of compression followed by decompression; see Section IV-F for full details. It was shown in [10] that for unweighted graphs, when a merge of two nodes requires those nodes to be within a given distance of each other then this tends to lead better fitness; furthermore, it was shown in [2] that this requirement also tends to allow for meaningful interpretation of the underlying data. We explore this concept in the current study for weighted graphs.…”

“…The E. coli gene regulatory network was compressed in [1] along with social communications networks, voting networks, and author collaborations. The application of graph compression to bioinformatics data including E. coli gene regulations, yeast transcription regulation, and proteinprotein interaction networks was examined in [10].…”

“…As in [10], all merges in the chromosome are required to be local, meaning that both nodes to be merged must be within a specified distance of each other. For the purposes of selecting nodes to merge, we consider only the number of edges and not their weights when computing distance.…”

“…This process may create fake edges in G that did not exist in G. The fitness function for unweighted graphs counts the number of fake edges created. For further details, see [10]. For example, consider the unweighted version of the graph shown in Fig.…”