Gene ontology terms visualization with dynamic distance-graph and similarity measures

In the biological field, having a visual and interactive representation of data is useful, particularly when there is a need to investigate a large amount of multilevel data. It is advantageous to communicate this knowledge intuitively because it helps the users to see the dynamic structure in which the correct connections are interacting and extrapolated. In this work, we propose a human-interaction system to view similarity data based on the functions of the Gene Ontology (Cellular Component, Molecular Function, and Biological Process) for Alzheimer’s and Parkinson’s dis- ease proteins/genes. The similarity data was built with the Lin and Wang measures for all three areas of gene ontology. We clustered data with the K-means algorithm and then we have suggested a dynamic and interactive view based on SigmaJS with the aim of allowing customization in the in- teractive mode of the analysis workflow by users. In this way we have obtained a more immediate visualization to capture the most relevant information within the three vocabularies of Gene Ontology. This facilitates to obtain an omic view and the possibility of carrying out a multilevel analysis with more details which is much more useful in order to better understand the knowledge of the end user.