Dynamically searching for a domain for protein function prediction

Hou, Jingyu and Jiang, Yongqing 2013, Dynamically searching for a domain for protein function prediction, Journal of bioinformatics and computational biology, vol. 11, no. 4, pp. 1-20, doi: 10.1142/S021972001350008X.

Attached Files
Name Description MIMEType Size Downloads

Title Dynamically searching for a domain for protein function prediction
Author(s) Hou, JingyuORCID iD for Hou, Jingyu orcid.org/0000-0002-6403-9786
Jiang, Yongqing
Journal name Journal of bioinformatics and computational biology
Volume number 11
Issue number 4
Start page 1
End page 20
Total pages 20
Publisher Imperial College Press
Place of publication London, England
Publication date 2013
ISSN 0219-7200
Keyword(s) dynamic prediction domain
protein function prediction
protein interaction
Summary The availability of large amounts of protein-protein interaction (PPI) data makes it feasible to use computational approaches to predict protein functions. The base of existing computational approaches is to exploit the known function information of annotated proteins in the PPI data to predict functions of un-annotated proteins. However, these approaches consider the prediction domain (i.e. the set of proteins from which the functions are predicted) as unchangeable during the prediction procedure. This may lead to valuable information being overwhelmed by the unavoidable noise information in the PPI data when predicting protein functions, and in turn, the prediction results will be distorted. In this paper, we propose a novel method to dynamically predict protein functions from the PPI data. Our method regards the function prediction as a dynamic process of finding a suitable prediction domain, from which representative functions of the domain are selected to predict functions of un-annotated proteins. Our method exploits the topological structural information of a PPI network and the semantic relationship between protein functions to measure the relationship between proteins, dynamically select a suitable prediction domain and predict functions. The evaluation on real PPI datasets demonstrated the effectiveness of our proposed method, and generated better prediction results.
Language eng
DOI 10.1142/S021972001350008X
Field of Research 080109 Pattern Recognition and Data Mining
080299 Computation Theory and Mathematics not elsewhere classified
Socio Economic Objective 890205 Information Processing Services (incl. Data Entry and Capture)
HERDC Research category C1 Refereed article in a scholarly journal
Persistent URL http://hdl.handle.net/10536/DRO/DU:30055265

Document type: Journal Article
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.

Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in TR Web of Science
Scopus Citation Count Cited 1 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 213 Abstract Views, 2 File Downloads  -  Detailed Statistics
Created: Tue, 27 Aug 2013, 11:48:46 EST

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.