Ir al contenido (pulsa Retorno)

Universitat Politècnica de Catalunya

    • Català
    • Castellano
    • English
    • LoginRegisterLog in (no UPC users)
  • mailContact Us
  • world English 
    • Català
    • Castellano
    • English
  • userLogin   
      LoginRegisterLog in (no UPC users)

UPCommons. Global access to UPC knowledge

Banner header
69.061 UPC E-Prints
You are here:
View Item 
  •   DSpace Home
  • E-prints
  • Programes de doctorat
  • Doctorat en Bioinformàtica
  • Articles de revista
  • View Item
  •   DSpace Home
  • E-prints
  • Programes de doctorat
  • Doctorat en Bioinformàtica
  • Articles de revista
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Probabilistic graphlets capture biological function in probabilistic molecular networks

Thumbnail
View/Open
Probabilistic_Graphlets_Word.pdf (1,751Mb)
 
10.1093/bioinformatics/btaa812
 
  View UPCommons Usage Statistics
  LA Referencia / Recolecta stats
Includes usage data since 2022
Cita com:
hdl:2117/346868

Show full item record
Doria Belenguer, Sergio
Youssef, Markus Kirolos
Böttcher, René
Malod-Dognin, Noël
Pržulj, Nataša
Document typeArticle
Defense date2020-12-29
PublisherOxford University Press
Rights accessOpen Access
All rights reserved. This work is protected by the corresponding intellectual and industrial property rights. Without prejudice to any existing legal exemptions, reproduction, distribution, public communication or transformation of this work are prohibited without permission of the copyright holder
Abstract
Motivation: Molecular interactions have been successfully modeled and analyzed as networks, where nodes represent molecules and edges represent the interactions between them. These networks revealed that molecules with similar local network structure also have similar biological functions. The most sensitive measures of network structure are based on graphlets. However, graphlet-based methods thus far are only applicable to unweighted networks, whereas real-world molecular networks may have weighted edges that can represent the probability of an interaction occurring in the cell. This information is commonly discarded when applying thresholds to generate unweighted networks, which may lead to information loss. Results: We introduce probabilistic graphlets as a tool for analyzing the local wiring patterns of probabilistic networks. To assess their performance compared to unweighted graphlets, we generate synthetic networks based on different well-known random network models and edge probability distributions and demonstrate that probabilistic graphlets outperform their unweighted counterparts in distinguishing network structures. Then we model different real-world molecular interaction networks as weighted graphs with probabilities as weights on edges and we analyze them with our new weighted graphlets-based methods. We show that due to their probabilistic nature, probabilistic graphlet-based methods more robustly capture biological information in these data, while simultaneously showing a higher sensitivity to identify condition-specific functions compared to their unweighted graphlet-based method counterparts.
CitationDoria, S. [et al.]. Probabilistic graphlets capture biological function in probabilistic molecular networks. "Bioinformatics (Oxford)", 29 Desembre 2020, vol. 36, núm. Supplement 2, p. i804-i812. 
URIhttp://hdl.handle.net/2117/346868
DOI10.1093/bioinformatics/btaa812
ISSN1367-4811
Publisher versionhttps://academic.oup.com/bioinformatics/article/36/Supplement_2/i804/6055922
Collections
  • Doctorat en Bioinformàtica - Articles de revista [13]
  View UPCommons Usage Statistics

Show full item record

FilesDescriptionSizeFormatView
Probabilistic_Graphlets_Word.pdf1,751MbPDFView/Open

Browse

This CollectionBy Issue DateAuthorsOther contributionsTitlesSubjectsThis repositoryCommunities & CollectionsBy Issue DateAuthorsOther contributionsTitlesSubjects

© UPC Obrir en finestra nova . Servei de Biblioteques, Publicacions i Arxius

info.biblioteques@upc.edu

  • About This Repository
  • Metadata under:Metadata under CC0
  • Contact Us
  • Send Feedback
  • Privacy Settings
  • Inici de la pàgina