A Network-Based Analysis of Disease Modules from a Taxonomic Perspective

Image credit: [Lorenzo Madeddu]


The aim of the study described in this paper is to shed more light on disease similarities by analyzing the relationship between categorical proximity in human-curated disease ontologies and proximity of disease modules in the human interactome network. We believe that the biomedical understanding of diseases is on the edge of a radical change. The disease module hypothesis (DMH), with its relevant applications to disease-gene discovery and drug repurposing, is leading the revolution of bio-medical research of the future. Human-curated disease ontologies are widely used for diagnostic evaluation, treatment and data comparisons over time, and clinical decision support. However, the recent results of DMH have so far only marginally influenced the disease categorization principles. For these reasons, we deem it fundamental to systematically analyze the degree of correspondence between the anatomical and histological principles at the basis of current disease ontologies and the pathobiological similarity relations discovered in recent network-based studies. Towards this objective, we define a methodology and related algorithms to automatically induce a hierarchical structure of disease modules from proximity relations in the interactome network, and to align, label and systematically compare this structure with a manually defined disease ontology. We demonstrate that our study has some relevant clinical implications: To identify promising regions of the human interactome where new disease-gene relationships could be discovered, either exploiting data-driven methods or clinical experiments; To identify unexplored molecular relationships among diseases; To extend, correct and refine human-curated taxonomies. To the best of our knowledge, this is the first work that presents a methodology to systematically integrate taxonomic and network-based disease classification principles.

In IEEE Journal of Biomedical and Health Informatics (JBHI)
Lorenzo Madeddu
Lorenzo Madeddu
Senior data scientist (R&D), PhD

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