A network-based analysis of disease modules from a taxonomic perspective

TL;DR

This paper introduces a network-based methodology to analyze disease similarities by comparing human-curated disease ontologies with the structural proximity of disease modules in the human interactome, aiding in refining classifications and discovering new disease-gene links.

Contribution

It presents a novel algorithmic approach to automatically derive hierarchical disease structures from network data and compare them with existing taxonomies.

Findings

The methodology reveals both alignments and discrepancies between structural and categorical disease similarities.

It helps refine disease classification systems by highlighting areas of agreement and divergence.

The approach identifies network regions promising for discovering new disease-gene interactions.

Abstract

Objective: Human-curated disease ontologies are widely used for diagnostic evaluation, treatment and data comparisons over time, and clinical decision support. The classification principles underlying these ontologies are guided by the analysis of observable pathological similarities between disorders, often based on anatomical or histological principles. Although, thanks to recent advances in molecular biology, disease ontologies are slowly changing to integrate the etiological and genetic origins of diseases, nosology still reflects this "reductionist" perspective. Proximity relationships of disease modules (hereafter DMs) in the human interactome network are now increasingly used in diagnostics, to identify pathobiologically similar diseases and to support drug repurposing and discovery. On the other hand, similarity relations induced from structural proximity of DMs also have several limitations, such as incomplete knowledge of disease-gene relationships and reliability of clinical trials to assess their validity. 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 DM in the interactome. Method: 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. Results: We demonstrate that the proposed methodology allows to systematically analyze commonalities and differences among structural and categorical similarity of human diseases, help refine and extend human disease classification systems, and identify promising network areas where new disease-gene interactions can be discovered.