A bayesian network model of proteins' association with promyelocytic leukemia (PML) nuclear bodies

Boden, Mikael, Dellaire, Graham, Burrage, Kevin and Bailey, Timothy L. (2010) A bayesian network model of proteins' association with promyelocytic leukemia (PML) nuclear bodies. Journal of Computational Biology, 17 4: 617-630.


Author Boden, Mikael
Dellaire, Graham
Burrage, Kevin
Bailey, Timothy L.
Title A bayesian network model of proteins' association with promyelocytic leukemia (PML) nuclear bodies
Journal name Journal of Computational Biology   Check publisher's open access policy
ISSN 1066-5277
1557-8666
Publication date 2010-04
Sub-type Article (original research)
DOI 10.1089/cmb.2009.0140
Volume 17
Issue 4
Start page 617
End page 630
Total pages 14
Editor Michael S. Waterman
Sorin Istrail
Place of publication New York, U.S.A.
Publisher Mary Ann Liebert
Collection year 2011
Language eng
Formatted abstract The modularity that nuclear organization brings has the potential to explain the function of aggregates of proteins and RNA. Promyelocytic leukemia nuclear bodies are implicated in important regulatory processes. To understand the complement of proteins associated with these intra-nuclear bodies, we construct a Bayesian network model that integrates sequence and protein-protein interaction data. The model predicts association with promyelocytic leukemia nuclear bodies accurately when interaction data is available. At a false positive rate of 10%, the true positive rate is almost 50%, indicated by an independent nuclear proteome reference set. The model provides strong support for further expanding the protein complement with several important regulators and a richer functional repertoire. Using special support vector machine (SVM)-nodes (equipped with string kernels), the Bayesian network is also able to produce predictions on the basis of sequence only, with an accuracy superior to that of baseline models. Supplementary Material is available online at www.liebertonline.com.
© Copyright 2010, Mary Ann Liebert, Inc..
Keyword Cancer genomics
Functional genomics
Proteins
Sequences
DNA-damage
Localisation
Database
Proteome
Kernels
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2011 Collection
Institute for Molecular Bioscience - Publications
 
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Created: Sun, 18 Jul 2010, 00:05:27 EST