Integrating Categorical and Structural Proximity in Disease Ontologies

Abstract

The purpose of the study described in this paper is to shed more light on disease similarities by analyzing the relationship between categorical proximity of diseases in human-curated ontologies and structural proximity of the related disease module (DM) in the interactome. We propose a methodology (and related algorithms) to automatically induce a hierarchical structure from proximity relations between DMs, and to compare this structure with a human-curated disease taxonomy.

Lorenzo Madeddu

Senior data scientist (R&D), PhD

He is a senior data scientist (R&D) in the Knowledge Graph Insights team at AstraZeneca.