Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-642-30191-9_16
DC FieldValue
dc.titleReconstruction of network evolutionary history from extant network topology and duplication history
dc.contributor.authorLi, S.
dc.contributor.authorChoi, K.P.
dc.contributor.authorWu, T.
dc.contributor.authorZhang, L.
dc.date.accessioned2014-05-19T02:56:59Z
dc.date.available2014-05-19T02:56:59Z
dc.date.issued2012
dc.identifier.citationLi, S.,Choi, K.P.,Wu, T.,Zhang, L. (2012). Reconstruction of network evolutionary history from extant network topology and duplication history. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 7292 LNBI : 165-176. ScholarBank@NUS Repository. <a href="https://doi.org/10.1007/978-3-642-30191-9_16" target="_blank">https://doi.org/10.1007/978-3-642-30191-9_16</a>
dc.identifier.isbn9783642301902
dc.identifier.issn03029743
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/53315
dc.description.abstractGenome-wide protein-protein interaction (PPI) data are readily available thanks to recent breakthroughs in biotechnology. However, PPI networks of extant organisms are only snapshots of the network evolution. How to infer the whole evolution history becomes a challenging problem in computational biology. In this paper, we present a likelihood-based approach to inferring network evolution history from the topology of PPI networks and the duplication relationship among the paralogs. Simulations show that our approach outperforms the existing ones in terms of the accuracy of reconstruction. Moreover, the growth parameters of several real PPI networks estimated by our method are more consistent with the ones predicted in literature. © 2012 Springer-Verlag.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1007/978-3-642-30191-9_16
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentSTATISTICS & APPLIED PROBABILITY
dc.contributor.departmentMATHEMATICS
dc.description.doi10.1007/978-3-642-30191-9_16
dc.description.sourcetitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.description.volume7292 LNBI
dc.description.page165-176
dc.identifier.isiutNOT_IN_WOS
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