The latent geometry of the human protein interaction network
- Publication type:
- Journal article
- Metadata:
-
- Autoren
- Gregorio Alanis-Lobato
- Pablo Mier
- Miguel Andrade-Navarro
- Autoren-URL
- https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=fis-test-1&SrcAuth=WosAPI&KeyUT=WOS:000441730900016&DestLinkType=FullRecord&DestApp=WOS_CPL
- DOI
- 10.1093/bioinformatics/bty206
- eISSN
- 1460-2059
- Externe Identifier
- Clarivate Analytics Document Solution ID: GQ5NB
- PubMed Identifier: 29635317
- ISSN
- 1367-4803
- Ausgabe der Veröffentlichung
- 16
- Zeitschrift
- BIOINFORMATICS
- Paginierung
- 2826 - 2834
- Datum der Veröffentlichung
- 2018
- Status
- Published
- Titel
- The latent geometry of the human protein interaction network
- Sub types
- Article
- Ausgabe der Zeitschrift
- 34
Data source: Web of Science (Lite)
- Other metadata sources:
-
- Abstract
- <jats:title>Abstract</jats:title> <jats:sec> <jats:title>Motivation</jats:title> <jats:p>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.</jats:p> </jats:sec> <jats:sec> <jats:title>Results</jats:title> <jats:p>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.</jats:p> </jats:sec> <jats:sec> <jats:title>Availability and implementation</jats:title> <jats:p>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.</jats:p> </jats:sec> <jats:sec> <jats:title>Supplementary information</jats:title> <jats:p>Supplementary data are available at Bioinformatics online.</jats:p> </jats:sec>
- Autoren
- Gregorio Alanis-Lobato
- Pablo Mier
- Miguel Andrade-Navarro
- DOI
- 10.1093/bioinformatics/bty206
- Editoren
- Bonnie Berger
- eISSN
- 1367-4811
- ISSN
- 1367-4803
- Ausgabe der Veröffentlichung
- 16
- Zeitschrift
- Bioinformatics
- Sprache
- en
- Online publication date
- 2018
- Paginierung
- 2826 - 2834
- Datum der Veröffentlichung
- 2018
- Status
- Published
- Herausgeber
- Oxford University Press (OUP)
- Herausgeber URL
- http://dx.doi.org/10.1093/bioinformatics/bty206
- Datum der Datenerfassung
- 2023
- Titel
- The latent geometry of the human protein interaction network
- Ausgabe der Zeitschrift
- 34
Data source: Crossref
- Abstract
- <h4>Motivation</h4>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.<h4>Results</h4>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.<h4>Availability and implementation</h4>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.<h4>Supplementary information</h4>Supplementary data are available at Bioinformatics online.
- Addresses
- Institute of Organismic and Molecular Evolution, Faculty of Biology, Johannes Gutenberg Universität, Mainz, Germany.
- Autoren
- Gregorio Alanis-Lobato
- Pablo Mier
- Miguel Andrade-Navarro
- DOI
- 10.1093/bioinformatics/bty206
- eISSN
- 1367-4811
- Externe Identifier
- PubMed Identifier: 29635317
- PubMed Central ID: PMC6084611
- Funding acknowledgements
- Johannes Gutenberg Universität:
- Open access
- true
- ISSN
- 1367-4803
- Ausgabe der Veröffentlichung
- 16
- Zeitschrift
- Bioinformatics (Oxford, England)
- Schlüsselwörter
- Humans
- Proteins
- Signal Transduction
- Algorithms
- Protein Interaction Maps
- Sprache
- eng
- Medium
- Open access status
- Open Access
- Paginierung
- 2826 - 2834
- Datum der Veröffentlichung
- 2018
- Status
- Published
- Publisher licence
- CC BY-NC
- Datum der Datenerfassung
- 2018
- Titel
- The latent geometry of the human protein interaction network.
- Sub types
- Research Support, Non-U.S. Gov't
- research-article
- Journal Article
- Ausgabe der Zeitschrift
- 34
Files
https://academic.oup.com/bioinformatics/article-pdf/34/16/2826/25441934/bty206.pdf https://europepmc.org/articles/PMC6084611?pdf=render
Data source: Europe PubMed Central
- 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.
- Date of acceptance
- 2018
- Autoren
- Gregorio Alanis-Lobato
- Pablo Mier
- Miguel Andrade-Navarro
- Autoren-URL
- https://www.ncbi.nlm.nih.gov/pubmed/29635317
- DOI
- 10.1093/bioinformatics/bty206
- eISSN
- 1367-4811
- Externe Identifier
- PubMed Central ID: PMC6084611
- Ausgabe der Veröffentlichung
- 16
- Zeitschrift
- Bioinformatics
- Schlüsselwörter
- Algorithms
- Humans
- Protein Interaction Maps
- Proteins
- Signal Transduction
- Sprache
- eng
- Country
- England
- Paginierung
- 2826 - 2834
- PII
- 4960047
- Datum der Veröffentlichung
- 2018
- Status
- Published
- Datum, an dem der Datensatz öffentlich gemacht wurde
- 2019
- Titel
- The latent geometry of the human protein interaction network.
- Sub types
- Journal Article
- Research Support, Non-U.S. Gov't
- Ausgabe der Zeitschrift
- 34
Data source: PubMed
- Beziehungen:
- Property of