title: Detection of composite communities in multiplex biological networks.
abstract: 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.
authors.affiliation: Centre for Process Systems Engineering, Department of Chemical Engineering,University College London, Torrington Place, London WC1E 7JE, United Kingdom.
authors.forename: Laura
authors.initials: L
authors.lastname: Bennett
authors.affiliation: Department of Informatics, Faculty of Natural and Mathematical Sciences, King's College London, Strand, London WC2R 2LS, UnitedKingdom.
authors.forename: Aristotelis
authors.initials: A
authors.lastname: Kittas
authors.affiliation: Department of Informatics, Faculty of Natural and Mathematical Sciences, King's College London, Strand, London WC2R 2LS, UnitedKingdom.
authors.forename: Gareth
authors.initials: G
authors.lastname: Muirhead
authors.affiliation: Centre for Process Systems Engineering, Department of Chemical Engineering,University College London, Torrington Place, London WC1E 7JE, United Kingdom.
authors.forename: Lazaros G
authors.initials: LG
authors.lastname: Papageorgiou
authors.affiliation: Department of Informatics, Faculty of Natural and Mathematical Sciences, King's College London, Strand, London WC2R 2LS, UnitedKingdom.
authors.forename: Sophia
authors.initials: S
authors.lastname: Tsoka
mesh_terms: D016000:Cluster Analysis; D053858:Metabolic Networks and Pathways; D008962:Models, Theoretical; D012441:Saccharomyces cerevisiae / Q000378:metabolism