Please use this identifier to cite or link to this item: https://doi.org/10.1142/S021972001230002X
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dc.titleA survey of computational methods for protein complex prediction from protein interaction networks
dc.contributor.authorSrihari, S.
dc.contributor.authorLeong, H.W.
dc.date.accessioned2013-07-04T08:49:08Z
dc.date.available2013-07-04T08:49:08Z
dc.date.issued2013
dc.identifier.citationSrihari, S., Leong, H.W. (2013). A survey of computational methods for protein complex prediction from protein interaction networks. Journal of Bioinformatics and Computational Biology 11 (2). ScholarBank@NUS Repository. https://doi.org/10.1142/S021972001230002X
dc.identifier.issn02197200
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/42332
dc.description.abstractComplexes of physically interacting proteins are one of the fundamental functional units responsible for driving key biological mechanisms within the cell. Their identification is therefore necessary to understand not only complex formation but also the higher level organization of the cell. With the advent of high-throughput techniques in molecular biology, significant amount of physical interaction data has been cataloged from organisms such as yeast, which has in turn fueled computational approaches to systematically mine complexes from the network of physical interactions among proteins (PPI network). In this survey, we review, classify and evaluate some of the key computational methods developed till date for the identification of protein complexes from PPI networks. We present two insightful taxonomies that reflect how these methods have evolved over the years toward improving automated complex prediction. We also discuss some open challenges facing accurate reconstruction of complexes, the crucial ones being the presence of high proportion of errors and noise in current high-throughput datasets and some key aspects overlooked by current complex detection methods. We hope this review will not only help to condense the history of computational complex detection for easy reference but also provide valuable insights to drive further research in this area. © 2013 Imperial College Press.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1142/S021972001230002X
dc.sourceScopus
dc.subjectProtein complex prediction
dc.subjectprotein interaction network
dc.subjectsparse complexes
dc.typeReview
dc.contributor.departmentCOMPUTER SCIENCE
dc.description.doi10.1142/S021972001230002X
dc.description.sourcetitleJournal of Bioinformatics and Computational Biology
dc.description.volume11
dc.description.issue2
dc.description.codenJBCBB
dc.identifier.isiut000321005100003
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