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Weighted Cohesiveness for Identification of Functional Modules and their Interconnectivity
University of Skövde, School of Humanities and Informatics.
University of Skövde, School of Humanities and Informatics.
University of Skövde, School of Humanities and Informatics.
2007 (English)In: Bioinformatics Research and Development: First International Conference, BIRD ’07, Springer Berlin/Heidelberg, 2007, 185-198 p.Conference paper, (Refereed)
Abstract [en]

Systems biology offers a holistic perspective where individual proteins are viewed as elements in a network of protein-protein interactions (PPI), in which the proteins have contextual functions within functional modules. In order to facilitate the identification and analysis of such modules, we have previously proposed a Gene Ontology-weighted clustering coefficient for identification of modules in PPI networks and a method, named SWEMODE (Semantic WEights for MODule Elucidation), where this measure is used to identify network modules. Here, we introduce novel aspects of the method that are tested and evaluated. One of the aspects that we consider is to use the k-core graph instead of the original protein-protein interaction graph.Also, by taking the spatial aspect into account, by using the GO cellular component annotation when calculating weighted cohesiveness, we are able to improve the results compared to previous work where only two of the GO aspects (molecular function and biological process) were combined. We here evaluate the predicted modules by calculating their overlap with MIPS functional complexes. In addition, we identify the “most frequent” proteins, i.e. the proteins that most frequently participate in overlapping modules. We also investigate the role of these proteins in the interconnectivity between modules. We find that the majority of identified proteins are involved in the assembly and arrangement of cell structures, such as the cell wall and cell envelope.

Place, publisher, year, edition, pages
Springer Berlin/Heidelberg, 2007. 185-198 p.
Series
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN 0302-9743, 1611-3349 ; 4414 LNBI
Identifiers
URN: urn:nbn:se:his:diva-2014DOI: 10.1007/978-3-540-71233-6_15ISI: 000246237400015Scopus ID: 2-s2.0-34548061879ISBN: 978-3-540-71232-9 OAI: oai:DiVA.org:his-2014DiVA: diva2:32290
Available from: 2008-04-28 Created: 2008-04-28 Last updated: 2013-03-18

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
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  • en-US
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Output format
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