ArticleBioinformatics (Oxford, England)2018
The latent geometry of the human protein interaction network.
Article in Bioinformatics (Oxford, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Hyperpathway: visualizing organization of pathway-molecule enriched interactions in omics studies via hyperbolic bipartite network embedding.NPJ systems biology and applications · 2026Article
- The role of protein embeddings for protein-protein interaction prediction with graph neural networks.Briefings in bioinformatics · 2026Article
- Unraveling cooperative and competitive interactions within protein triplets in the human interactome.Scientific reports · 2025Article
- Effective data visualization strategies in untargeted metabolomics.Natural product reports · 2025Review
- Topology-driven negative sampling enhances generalizability in protein-protein interaction prediction.Bioinformatics (Oxford, England) · 2025Article
- Article
- Simplicity within biological complexity.Bioinformatics advances · 2025Article
- Article
- Selection of Multi-Drug Targets against Drug-ResistantInternational journal of molecular sciences · 2023Article
- Finding shortest and nearly shortest path nodes in large substantially incomplete networks by hyperbolic mapping.Nature communications · 2023Article
- HetIG-PreDiG: A Heterogeneous Integrated Graph Model for Predicting Human Disease Genes based on gene expression.PloS one · 2023Article
- Graph embedding and geometric deep learning relevance to network biology and structural chemistry.Frontiers in artificial intelligence · 2023Review
- How the latent geometry of a biological network provides information on its dynamics: the case of the gene network of chronic myeloid leukaemia.Frontiers in cell and developmental biology · 2023Article
- Analysis of Huntington's Disease Modifiers Using the Hyperbolic Mapping of the Protein Interaction Network.International journal of molecular sciences · 2022Article
- Performance Assessment of the Network Reconstruction Approaches on Various Interactomes.Frontiers in molecular biosciences · 2021Article
- Novel metric for hyperbolic phylogenetic tree embeddings.Biology methods & protocols · 2021Article
- Hyperbolic mapping of human proximity networks.Scientific reports · 2020Article
- Navigable maps of structural brain networks across species.PLoS computational biology · 2020Article
- To Embed or Not: Network Embedding as a Paradigm in Computational Biology.Frontiers in genetics · 2019Review
- Geometric characterisation of disease modules.Applied network science · 2018Article
Corrections and comments
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3 authors.
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Abstract
Motivation: A series of recently introduced algorithms and models advocates for the existence of a hyperbolic geometry underlying the network representation of complex systems. Since the human protein interaction network (hPIN) has a complex architecture, we hypothesized that uncovering its latent geometry could ease challenging problems in systems biology, translating them into measuring distances between proteins. Results: We embedded the hPIN to hyperbolic space and found that the inferred coordinates of nodes capture biologically relevant features, like protein age, function and cellular localization. This means that the representation of the hPIN in the two-dimensional hyperbolic plane offers a novel and informative way to visualize proteins and their interactions. We then used these coordinates to compute hyperbolic distances between proteins, which served as likelihood scores for the prediction of plausible protein interactions. Finally, we observed that proteins can efficiently communicate with each other via a greedy routing process, guided by the latent geometry of the hPIN. We show that these efficient communication channels can be used to determine the core members of signal transduction pathways and to study how system perturbations impact their efficiency. Availability and implementation: An R implementation of our network embedder is available at https://github.com/galanisl/NetHypGeom. Also, a web tool for the geometric analysis of the hPIN accompanies this text at http://cbdm-01.zdv.uni-mainz.de/~galanisl/gapi. Supplementary information: Supplementary data are available at Bioinformatics online.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.