Please use this identifier to cite or link to this item: https://doi.org/10.1111/j.1749-6632.2008.03760.x
Title: A probabilistic graph-theoretic approach to integrate multiple predictions for the protein-protein subnetwork prediction challenge
Authors: Chua, H.N.
Hugo, W. 
Liu, G. 
Li, X.
Wong, L. 
Ng, S.-K.
Keywords: Data integration
Data mining
Protein-protein interactions
Issue Date: 2009
Source: Chua, H.N., Hugo, W., Liu, G., Li, X., Wong, L., Ng, S.-K. (2009). A probabilistic graph-theoretic approach to integrate multiple predictions for the protein-protein subnetwork prediction challenge. Annals of the New York Academy of Sciences 1158 : 224-233. ScholarBank@NUS Repository. https://doi.org/10.1111/j.1749-6632.2008.03760.x
Abstract: The protein-protein subnetwork prediction challenge presented at the 2nd Dialogue for Reverse Engineering Assessments and Methods (DREAM2) conference is an important computational problem essential to proteomic research. Given a set of proteins from the Saccharomyces cerevisiae (baker's yeast) genome, the task is to rank all possible interactions between the proteins from the most likely to the least likely. To tackle this task, we adopt a graph-based strategy to combine multiple sources of biological data and computational predictions. Using training and testing sets extracted from existing yeast protein-protein interactions, we evaluate our method and show that it can produce better predictions than any of the individual data sources. This technique is then used to produce our entry for the protein-protein subnetwork prediction challenge. © 2009 New York Academy of Sciences.
Source Title: Annals of the New York Academy of Sciences
URI: http://scholarbank.nus.edu.sg/handle/10635/39644
ISBN: 9781573317511
ISSN: 00778923
DOI: 10.1111/j.1749-6632.2008.03760.x
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

8
checked on Dec 5, 2017

WEB OF SCIENCETM
Citations

7
checked on Dec 5, 2017

Page view(s)

46
checked on Dec 11, 2017

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.