TY - GEN
T1 - Construction and analysis of genome-wide SNP networks
AU - Liu, Yang
AU - Zhou, Jin
AU - Liu, Zhiping
AU - Chen, Luonan
AU - Ng, Michael K.
PY - 2012
Y1 - 2012
N2 - The study of gene regulatory network (GRN) and protein protein interaction network (PPI) is believed to be fundamental to the understanding of molecular processes and functions in system biology and therefore, attracted more and more attentions in past few years. However, there is little focus about network construction in single nucleotide polymorphism (SNP) level, which may provide a direct insight into mutations among individuals, potentially leading to new pathogenesis discovery and diagnostics. In this paper, we present a novel method to mine, model and evaluate a SNP sub-network from SNP-SNP interactions. Specifically, based on logistic regression between two SNPs, we first construct a genome-wide SNP-SNP interaction network. Then by using gene information, selected SNP seeds are employed to detect SNP sub-networks with a maximal modularity. Finally to identify functional role of each SNP sub-network, its gene association network is constructed and their functional similarity values are calculated to show the biological relevance. Results show that our method is effective in SNP sub-network extraction and gene function prediction.
AB - The study of gene regulatory network (GRN) and protein protein interaction network (PPI) is believed to be fundamental to the understanding of molecular processes and functions in system biology and therefore, attracted more and more attentions in past few years. However, there is little focus about network construction in single nucleotide polymorphism (SNP) level, which may provide a direct insight into mutations among individuals, potentially leading to new pathogenesis discovery and diagnostics. In this paper, we present a novel method to mine, model and evaluate a SNP sub-network from SNP-SNP interactions. Specifically, based on logistic regression between two SNPs, we first construct a genome-wide SNP-SNP interaction network. Then by using gene information, selected SNP seeds are employed to detect SNP sub-networks with a maximal modularity. Finally to identify functional role of each SNP sub-network, its gene association network is constructed and their functional similarity values are calculated to show the biological relevance. Results show that our method is effective in SNP sub-network extraction and gene function prediction.
UR - http://www.scopus.com/inward/record.url?scp=84868635248&partnerID=8YFLogxK
U2 - 10.1109/ISB.2012.6314158
DO - 10.1109/ISB.2012.6314158
M3 - Conference proceeding
AN - SCOPUS:84868635248
SN - 9781467343985
T3 - 2012 IEEE 6th International Conference on Systems Biology, ISB 2012
SP - 327
EP - 332
BT - 2012 IEEE 6th International Conference on Systems Biology, ISB 2012
T2 - 2012 IEEE 6th International Conference on Systems Biology, ISB 2012
Y2 - 18 August 2012 through 20 August 2012
ER -