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Detection of composite communities in multiplex biological networks

Research output: Contribution to journalArticle

Laura Bennett, Aristotelis Kittas, Gareth Muirhead, Lazaros G. Papageorgiou, Sophia Tsoka

Original languageEnglish
Article number10345
JournalScientific Reports
Early online date27 May 2015
Accepted/In press26 Mar 2015
E-pub ahead of print27 May 2015


  • srep10345

    srep10345.pdf, 1.99 MB, application/pdf

    Uploaded date:23 Mar 2016

    Version:Final published version

    Licence:CC BY

King's Authors


The detection of community structure is a widely accepted means of investigating the principles governing biological systems. Recent efforts are exploring ways in which multiple data sources can be integrated to generate a more comprehensive model of cellular interactions, leading to the detection of more biologically relevant communities. In this work, we propose a mathematical programming model to cluster multiplex biological networks, i.e. multiple network slices, each with a different interaction type, to determine a single representative partition of composite communities. Our method, known as SimMod, is evaluated through its application to yeast networks of physical, genetic and co-expression interactions. A comparative analysis involving partitions of the individual networks, partitions of aggregated networks and partitions generated by similar methods from the literature highlights the ability of SimMod to identify functionally enriched modules. It is further shown that SimMod offers enhanced results when compared to existing approaches without the need to train on known cellular interactions.

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