Large-scale analysis of disease pathways in the human interactome

By Monica Agrawal, Marinka Zitnik, Jure Leskovec. 2017

121 citations recorded by the source corpus.

Discovering disease pathways, which can be defined as sets of proteins associated with a given disease, is an important problem that has the potential to provide clinically actionable insights for disease diagnosis, prognosis, and treatment. Computational methods aid the discovery by relying on protein-protein interaction (PPI) networks. They start with a few known disease-associated proteins and aim to find the rest of the pathway by exploring the PPI network around the known disease proteins. However, the success of such methods has been limited, and failure cases have not been well understood. Here we study the PPI network structure of 519 disease pathways. We find that 90% of pathways do not correspond to single well-connected components in the PPI network. Instead, proteins associated with a single disease tend to form many separate connected components/regions in the network. We then evaluate state-of-the-art disease pathway discovery methods and show that their performance is especially poor on diseases with disconnected pathways. Thus, we conclude that network connectivity structure alone may not be sufficient for disease pathway discovery. However, we show that higher-order network structures, such as small subgraphs of the pathway, provide a promising direction for the development of new methods.

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Citations in the corpus

Evolution of resilience in protein interactomes across the tree of life (2018, background): “As more detailed information about functions of individual proteins in the interactome (43), as well as dynamic protein-expression data (44), becomes available, our measure of interactome resilience could be adapted to give a more complex definition of resiliency, which might yield more detailed evo”
Prioritizing network communities (2018, background): “…it would be especially interesting to apply it to networks, where rich meta information exists and interesting domain-speci ﬁ c scoring metrics can be developed, such as protein interaction networks with disease pathway meta information 33 , and molecular networks with genome-wide associations 34 .”
GNNGuard: Defending Graph Neural Networks against Adversarial Attacks (2020, methodology): “We use a Disease Pathway (DP) [48] graph with continuous features describing a system”
Graph convolutional networks for computational drug development and discovery (2019, background): “For example, in a disease–protein network analysis [119], the authors found that disease pathways do not correspond to single well-connected components and higher-ordernetworkstructures(motifs)provideadditionalinformation to disease pathway discovery.”
Graph Representation Learning in Biomedicine and Healthcare (2021, unknown): “According to the disease-module hypothesis 13 , cellular components (such as genes, proteins or metabolites associated with a specific disease) tend to cluster in the same network neighbourhood 16 .”
Graph Representation Learning (2020, background): “The many real-world applications of community detection include uncovering functional modules in genetic interaction networks [Agrawal et al., 2018] and uncovering fraudulent groups of users in financial transaction networks [Pandit et al.”
GNNExplainer: Generating Explanations for Graph Neural Networks (2019, result): “6, 49], graph clustering [14, 46] or graph classification [12, 16, 46]. These tasks occur in domains where the graph structure is ubiquitous, such as social networks [5], content graphs [45], biology [3], and chemoinformatics [16, 25, 54]. Recent instances of GNN models have been proposed to augment”
Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development (2021, background): “Machine learning has already proved useful in the search of antibiotics [136], polypharmacy [176], drug repurposing for emerging diseases [47], protein folding and design [64, 41], and biomolecular interactions [177, 3, 55, 39].”