Iteratively predict protein functions from protein-protein interactions

Chi, Xiaoxiao and Hou, Jingyu 2012, Iteratively predict protein functions from protein-protein interactions. In Zhu, Egui and Sambath, Sabo (ed), Information technology and agricultural engineering, Springer-Verlag Berlin Heidelberg, Berlin, Germany, pp.771-778, doi: 10.1007/978-3-642-27537-1_91.

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Title Iteratively predict protein functions from protein-protein interactions
Author(s) Chi, Xiaoxiao
Hou, JingyuORCID iD for Hou, Jingyu
Title of book Information technology and agricultural engineering
Editor(s) Zhu, Egui
Sambath, Sabo
Publication date 2012
Series Advances in intelligent and soft computing; v.134
Chapter number 91
Total chapters 117
Start page 771
End page 778
Total pages 8
Publisher Springer-Verlag Berlin Heidelberg
Place of Publication Berlin, Germany
Keyword(s) functional prediction
gene ontology
iterative method
protein-protein interaction
Summary Current similarity-based approaches of predicting protein functions from protein-protein interaction (PPI) data usually make use of available information in the PPI network to predict functions of un-annotated proteins, and the prediction is a one-off procedure. However the interactions between proteins are more likely to be mutual rather than static and mono-directed. In other words, the un-annotated proteins, once their functions are predicted, will in turn affect the similarities between proteins. In this paper, we propose an innovative iteration algorithm that incorporates this dynamic feature of protein interaction into the protein function prediction, aiming to achieve higher prediction accuracies and get more reasonable results. With our algorithm, instead of one-off function predictions, functions are assigned to an unannotated protein iteratively until the functional similarities between proteins achieve a stable state. The experimental results show that our iterative method can provide better prediction results than one-off prediction methods with higher prediction accuracies, and is stable for large protein datasets.
Notes first published in 2010 in CMBB 2010 : Proceedings of the First International Conference on Cellular, Molecular Biology, Biophysics and Bioengineering, Qiquihar, China , 25-26 Dec. 2010.
ISBN 9783642275364
ISSN 1867-5662
Language eng
DOI 10.1007/978-3-642-27537-1_91
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 890205 Information Processing Services (incl. Data Entry and Capture)
HERDC Research category B1.1 Book chapter
Copyright notice ©2012, Springer
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Document type: Book Chapter
Collection: School of Information Technology
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