Construction and analysis of single nucleotide polymorphism–single nucleotide polymorphism interaction networks. Issue 5 (1st October 2013)

• Record Type: Journal Article
• Authors: Liu, Yang; Li, Xutao; Liu, Zhiping; Chen, Luonan; Ng, Michael K.
• Is Part Of: IET systems biology. Volume 7:Issue 5(2013)
• Page Start: 170
• Page End: 181
• Publication Date: 2013-10-01
• Subjects: biology computing -- blood vessels -- diseases -- DNA -- genetics -- genomics -- molecular biophysics -- molecular configurations -- polymorphism -- proteins -- RNA; disease genes -- coronary artery disease -- datasets -- Parkinson disease -- complex diseases -- pathogenetic mechanisms -- genetic characters -- systems biology functions -- molecular processes -- protein‐protein interaction network -- gene regulatory network -- single nucleotide polymorphism interaction networks; Systems biology -- Periodicals; Cell physiology -- Periodicals; Biological systems -- Mathematical models -- Periodicals; Genetics -- Mathematical models -- Periodicals; Computational biology -- Periodicals; 573
• DOI: 10.1049/iet-syb.2012.0055
• Languages: English
• ISSNs: 1751-8849

Abstract

Abstract : The study of gene regulatory network and protein–protein interaction network is believed to be fundamental to the understanding of molecular processes and functions in systems biology. In this study, the authors are interested in single nucleotide polymorphism (SNP) level and construct SNP–SNP interaction network to understand genetic characters and pathogenetic mechanisms of complex diseases. The authors employ existing methods to mine, model and evaluate a SNP sub‐network from SNP–SNP interactions. In the study, the authors employ the two SNP datasets: Parkinson disease and coronary artery disease to demonstrate the procedure of construction and analysis of SNP–SNP interaction networks. Experimental results are reported to demonstrate the procedure of construction and analysis of such SNP–SNP interaction networks can recover some existing biological results and related disease genes.