Comparing functional visualizations of genes

Biological experiments identify large lists of genes and biologists find functional relationships between them to get a better understanding of data. Gene Ontology (GO) is a database of terms related to genes and gene products that has the potential to assist in visualizing and finding functional relationships between genes. We augment genes with GO terms and compare visualizations using two term–to– term similarity measures for terms associated with genes: a hop-based distance measure and an information-content-based similarity measure (IC). Visualization is with Singular Value Decomposition. Relationships are further explained using Pearson correlation with GO terms. Results show that both methods find the relationships between genes however, difference is observed in visualization of GO terms, where IC method shows tightly-packed clusters in contrast to the loose and scattered clusters found with hop based method.