Iteratively predict protein functions from protein-protein interactions

Chi, Xiaoxiao and Hou, Jingyu 2010, Iteratively predict protein functions from protein-protein interactions, in CMBB 2010 : Proceedings of the First International Conference on Cellular, Molecular Biology, Biophysics and Bioengineering, CMBB, [Qiqihar, China], pp. 219-222.

Attached Files
Name Description MIMEType Size Downloads

Title Iteratively predict protein functions from protein-protein interactions
Author(s) Chi, Xiaoxiao
Hou, Jingyu
Conference name Cellular, Molecular Biology, Biophysics and Bioengineering. Conference (1st : 2010 : Qiqihar, China)
Conference location Qiqihar, China
Conference dates 25-26 Dec. 2010
Title of proceedings CMBB 2010 : Proceedings of the First International Conference on Cellular, Molecular Biology, Biophysics and Bioengineering
Editor(s) Zhou, Xueli
Publication date 2010
Conference series Cellular, Molecular Biology, Biophysics and Bioengineering Conference
Start page 219
End page 222
Total pages 4
Publisher CMBB
Place of publication [Qiqihar, China]
Keyword(s) functional prediction
iterative method
protein-protein interaction
gene ontology
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 that 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.

ISBN 9781424491582
9781424491599
9781424491612
Language eng
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 890205 Information Processing Services (incl. Data Entry and Capture)
HERDC Research category E1 Full written paper - refereed
HERDC collection year 2010
Copyright notice ©2010, CMBB
Persistent URL http://hdl.handle.net/10536/DRO/DU:30033765

Document type: Conference Paper
Collection: School of Information Technology
Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Versions
Version Filter Type
Access Statistics: 180 Abstract Views, 5 File Downloads  -  Detailed Statistics
Created: Wed, 06 Apr 2011, 16:18:57 EST by Sandra Dunoon

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.