Objective: Human-curated diseaseontologies 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 DMs in the interactome. Method: We propose a method (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 method 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.

中文翻译：

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从分类学角度对疾病模块进行基于网络的分析

目的：人类策划的疾病本体广泛用于诊断评估、治疗和数据比较，以及临床决策支持。这些本体论的分类原则以对疾病之间可观察到的病理学相似性的分析为指导，通常基于解剖学或组织学原则。尽管由于分子生物学的最新进展，疾病本体正在慢慢改变以整合疾病的病因和遗传起源，但疾病学仍然反映了这种“还原论”的观点。人类相互作用组网络中疾病模块（以下称为 DM）的邻近关系现在越来越多地用于诊断，以识别病理生物学上相似的疾病并支持药物再利用和发现。另一方面，由 DM 的结构接近引起的相似关系也有一些局限性，例如对疾病基因关系的不完全了解和临床试验的可靠性以评估其有效性。本文描述的研究的目的是通过分析人类策划的本体中疾病的类别接近度与相互作用组中相关 DM 的结构接近度之间的关系，进一步阐明疾病的相似性。方法：我们提出了一种方法（和相关算法），可以从 DM 之间的邻近关系中自动推断出层次结构，并将该结构与人类策划的疾病分类法进行比较。结果：我们证明所提出的方法可以系统地分析人类疾病的结构和分类相似性之间的共性和差异，帮助改进和扩展人类疾病分类系统，并确定可以发现新疾病-基因相互作用的有前景的网络区域。