Paper Info
Title
A non-parameter Ising model for network-based identification of differentially expressed genes in recurrent breast cancer patients
Abstract
Identification of genes and pathways involving in diseases and physiological conditions is a major task in systems biology. In this study, we develop a new non-parameter Ising model to integrate protein-protein interaction network and microarray data for identifying differentially expressed (DE) genes. We also propose a simulated annealing algorithm to find the optimal configuration of the Ising model. We test the Ising model to two breast cancer microarray data sets. The results show that more cancer related differentially expressed subnetworks and genes are identified by the Ising model than by the Markov random filed (MRF) model.
Year
DOI
Venue
2010
10.1109/BIBM.2010.5706565
BIBM
Keywords
Field
DocType
protein-protein interaction network,gynaecology,network based identification,markov random field model,genetics,differentially expressed genes,gene identification,simulated annealing algorithm,proteins,ising model,complex networks,molecular biophysics,differentially expressed subnetworks,cancer,recurrent breast cancer patients,disease pathway identification,systems biology,medical computing,biological tissues,ising model optimal configuration,simulated annealing,markov processes,biological organs,breast cancer microarray data sets,nonparameter ising model,system biology,breast cancer,data models,microarray data
Data modeling,Markov process,Computer science,Markov chain,Systems biology,Interaction network,Microarray analysis techniques,Ising model,Artificial intelligence,Bioinformatics,Cancer,Machine learning
Conference
ISSN
ISBN
Citations 
2156-1125
978-1-4244-8307-5
0
PageRank 
References 
Authors
0.34
3
5
Name
Order
Citations
PageRank
Xumeng Li100.34
Frank Alex Feltus2302.59
Xiaoqian Sun3245.63
James Zijun Wang4154.84
Feng Luo528426.03