Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11861/7425
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dc.contributor.authorCheng, Lixinen_US
dc.contributor.authorLiu, Pengfeien_US
dc.contributor.authorProf. LEUNG Kwong Saken_US
dc.date.accessioned2023-02-22T10:48:23Z-
dc.date.available2023-02-22T10:48:23Z-
dc.date.issued2018-04-
dc.identifier.citationIET Systems Biology ,2018, vol. 12(2), pp. 55-61en_US
dc.identifier.urihttp://hdl.handle.net/20.500.11861/7425-
dc.description.abstractComputational clustering methods help identify functional modules in protein–protein interaction (PPI) network, in which proteins participate in the same biological pathways or specific functions. Subcellular localisation is crucial for proteins to implement biological functions and each compartment accommodates specific portions of the protein interaction structure. However, the importance of protein subcellular localisation is often neglected in the studies of module identification. In this study, the authors propose a novel procedure, subcellular module identification with localisation expansion (SMILE), to identify super modules that consist of several subcellular modules performing specific biological functions among cell compartments. These super modules identified by SMILE are more functionally diverse and have been verified to be more associated with known protein complexes and biological pathways compared with the modules identified from the global PPI networks in both the compartmentalised PPI and InWeb_InBioMap datasets. The authors’ results reveal that subcellular localisation is a principal feature of functional modules and offers important guidance in detecting biologically meaningful results.en_US
dc.language.isoenen_US
dc.relation.ispartofIET Systems Biologyen_US
dc.titleSMILE: a novel procedure for subcellular module identification with localisation expansionen_US
dc.typePeer Reviewed Journal Articleen_US
dc.identifier.doi10.1049/iet-syb.2017.0085-
item.fulltextNo Fulltext-
crisitem.author.deptDepartment of Applied Data Science-
Appears in Collections:Applied Data Science - Publication
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