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Going the Distance for Protein Function Prediction: A New Distance Metric for Protein Interaction Networks
Open accessAbstract
In protein-protein interaction (PPI) networks, functional similarity is often inferred based on the function of directly interacting proteins, or more generally, some notion of interaction network proximity among proteins in a local neighborhood. Prior methods typically measure proximity as the shortest-path distance in the network, but this has only a limited ability to capture fine-grained neighborhood distinctions, because most proteins are close to each other, and there are many ties in proximity. We introduce diffusion state distance (DSD), a new metric based on a graph diffusion property, designed to capture finer-grained distinctions in proximity for transfer of functional annotation in PPI networks. We present a tool that, when input a PPI network, will output the DSD distances between every pair of proteins. We show that replacing the shortest-path metric by DSD improves the performance of classical function prediction methods across the board.
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“To provide therapeutic predictions, geometric deep-learning models optimized on large medical KGs15 can match disease signatures to therapeutic candidates based on networks perturbed in disease15–18.”
From A foundation model for clinician-centered drug repurposing (2024) · cites this paper as [17]
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“They have been built as a hierarchical graph that GNNs then leverage to learn dependencies of the terms [126], or directly used as protein function labels [106, 127].”
From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [126]
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“Gene Ontology (GO) terms [125] are a standardized vocabulary for describing molecular functions, biological processes, and cellular locations of gene products [126].”
From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [126]
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Metadata
- Authors
- Cao, M., Zhang, H., Park, J., Daniels, N. M., Crovella, M. E., Cowen, L. J., & Hescott, B.
- Affiliations
- Tufts University (US)
- Boston University (US)
- Corresponding author
- Cowen, Lenore J.
- Hescott, Benjamin
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2013-10-23
- Volume
- 8
- Issue
- 10
- Pages
- e76339
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC3806810
- OpenAlex
- W1990223774
- Cited by
- 131
- References
- 29
- Citations by year
- 2026: 4 · 2025: 8 · 2024: 6 · 2023: 7 · 2022: 17 · 2021: 23 · 2020: 11 · 2019: 16 · 2018: 14 · 2017: 9 · 2016: 7 · 2015: 5 · 2014: 4
- FWCI
- 2.39
- Type
- journal article
- Language
- en
- Update notices
- correction (2013-10-30)
- Open access
- gold
- doi.org
- Locations
- PLoS ONE (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- OpenBU (Boston University) (submittedVersion, cc-by)
- Funders
- National Science Foundation (0905565, 1012910, 1018266, 1117039)
- Ministerio de Economía y Competitividad
- National Institutes of Health (R01‐grant, 5r01gm080330-04)
- National Institute of General Medical Sciences (R01GM080330, GM080330)
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (R01HD058880)
- Army Research Office (W911NF-11-1-0227, W911NF, W911NF-11)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Keywords
- protein-protein interaction network, protein function prediction, distance measures, graph diffusion, shortest path length, network proximity
- MeSH terms
- Algorithms, Models, Genetic, Proteins, Protein Interaction Maps
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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