Assessment of community efforts to advance network-based prediction of protein-protein interactions.

Comprehensive understanding of the human protein-protein interaction (PPI) network, aka the human interactome, can provide important insights into the molecular mechanisms of complex biological processes and diseases. Despite the remarkable experimental efforts undertaken to date to determine the structure of the human interactome, many PPIs remain unmapped. Computational approaches, especially network-based methods, can facilitate the identification of previously uncharacterized PPIs. Many such methods have been proposed. Yet, a systematic evaluation of existing network-based methods in predicting PPIs is still lacking. Here, we report community efforts initiated by the International Network Medicine Consortium to benchmark the ability of 26 representative network-based methods to predict PPIs across six different interactomes of four different organisms: A. thaliana, C. elegans, S. cerevisiae, and H. sapiens. Through extensive computational and experimental validations, we found that advanced similarity-based methods, which leverage the underlying network characteristics of PPIs, show superior performance over other general link prediction methods in the interactomes we considered.

A Network-Based Analysis of Disease Modules From a Taxonomic Perspective.

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.

Integrating categorical and structural proximity in Disease Ontologies.

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.Clinical relevance- Disease ontologies are extensively used for diagnostic evaluation and clinical decision support but still reflect the clinical reductionist perspective. We demonstrate that the proposed network-based methodology allows us to analyze commonalities and differences among structural and categorical similarity of human diseases, help refine human disease classification systems, and identify promising network areas where new disease-gene interactions can be discovered.