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Sci Rep. 2019 Mar 11;9(1):4025. doi: 10.1038/s41598-019-40368-1.

Quantitative evaluation of ontology design patterns for combining pathology and anatomy ontologies.

Author information

1
King Abdullah University of Science and Technology, Computer, Electrical & Mathematical Sciences and Engineering Division, Computational Bioscience Research Center, Thuwal, 23955-6900, Saudi Arabia.
2
King Abdul-Aziz University, Faculty of Computing and Information Technology, Rabigh, 25732, Saudi Arabia.
3
The Jackson Laboratory, 600, Main Street, Bar Harbor, ME, 04609, USA.
4
The Jackson Laboratory, 600, Main Street, Bar Harbor, ME, 04609, USA. pns12@cam.ac.uk.
5
Department of Physiology, Development & Neuroscience, University of Cambridge, Downing Street, Cambridge, CB2 3EG, UK. pns12@cam.ac.uk.
6
King Abdullah University of Science and Technology, Computer, Electrical & Mathematical Sciences and Engineering Division, Computational Bioscience Research Center, Thuwal, 23955-6900, Saudi Arabia. robert.hoehndorf@kaust.edu.sa.

Abstract

Data are increasingly annotated with multiple ontologies to capture rich information about the features of the subject under investigation. Analysis may be performed over each ontology separately, but recently there has been a move to combine multiple ontologies to provide more powerful analytical possibilities. However, it is often not clear how to combine ontologies or how to assess or evaluate the potential design patterns available. Here we use a large and well-characterized dataset of anatomic pathology descriptions from a major study of aging mice. We show how different design patterns based on the MPATH and MA ontologies provide orthogonal axes of analysis, and perform differently in over-representation and semantic similarity applications. We discuss how such a data-driven approach might be used generally to generate and evaluate ontology design patterns.

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