Statistical tests for associations between two directed acyclic graphs.
Biological data, and particularly annotation data, are increasingly being represented in directed acyclic graphs (DAGs). However, while relevant biological information is implicit in the links between multiple domains, annotations from these different domains are usually represented in distinct, unconnected DAGs, making links between the domains represented difficult to determine. We develop a novel family of general statistical tests for the discovery of strong associations between two directed acyclic graphs. Our method takes the topology of the input graphs and the specificity and relevance of associations between nodes into consideration. We apply our method to the extraction of associations between biomedical ontologies in an extensive use-case. Through a manual and an automatic evaluation, we show that our tests discover biologically relevant relations. The suite of statistical tests we develop for this purpose is implemented and freely available for download.
Hoehndorf
Robert
R
Research Group Ontologies in Medicine, Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany. hoehndor@ebi.ac.uk
Ngonga Ngomo
Axel-Cyrille
AC
Dannemann
Michael
M
Kelso
Janet
J
D015233:Models, Statistical