ArticlePacific Symposium on Biocomputing. Pacific Symposium on Biocomputing2018
Large-scale analysis of disease pathways in the human interactome.
Article in Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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Who cites it
38 citing papers in PubMed.
- The role of protein embeddings for protein-protein interaction prediction with graph neural networks.Briefings in bioinformatics · 2026Article
- Powers of magnetic graph matrix: Fourier spectrum, walk compression, and applications.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- RISK: a next-generation tool for biological network annotation and visualization.Bioinformatics (Oxford, England) · 2026Article
- Learning Universal Representations of Intermolecular Interactions with ATOMICA.bioRxiv : the preprint server for biology · 2025Article
- GRPhIN: graphlet characterization of regulatory and physical interaction networks.Bioinformatics advances · 2025Article
- Heterogeneous network approaches to protein pathway prediction.Computational and structural biotechnology journal · 2024Review
- Disease gene prioritization with quantum walks.Bioinformatics (Oxford, England) · 2024Article
- Contextual AI models for single-cell protein biology.Nature methods · 2024Article
- Non-Coding RNAs Extended Omnigenic Module of Cancers.Entropy (Basel, Switzerland) · 2024Article
- Contextual AI models for single-cell protein biology.bioRxiv : the preprint server for biology · 2024Article
- Prioritization of oligogenic variant combinations in whole exomes.Bioinformatics (Oxford, England) · 2024Article
- Current and future directions in network biology.Bioinformatics advances · 2024Article
- Single cell phototransfection of mRNAs encoding SARS-CoV2 spike and nucleocapsid into human astrocytes results in RNA dependent translation interference.Frontiers in drug delivery · 2024Article
- Evaluation of input data modality choices on functional gene embeddings.NAR genomics and bioinformatics · 2023Article
- Assessment of GO-Based Protein Interaction Affinities in the Large-Scale Human-Coronavirus Family Interactome.Vaccines · 2023Article
- A novel strategy for dynamic modeling of genome-scale interaction networks.Bioinformatics (Oxford, England) · 2023Article
- Identification of disease modules using higher-order network structure.Bioinformatics advances · 2023Article
- Unraveling patient heterogeneity in complex diseases through individualized co-expression networks: a perspective.Frontiers in genetics · 2023Article
- Computational modeling of human-nCoV protein-protein interaction network.Methods (San Diego, Calif.) · 2022Article
- Drug repurposing for COVID-19 using computational screening: Is Fostamatinib/R406 a potential candidate?Methods (San Diego, Calif.) · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
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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29218874PMC5731453What OpenQuestion holds
Registered trials
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.