TY - DATA T1 - Spectrum analysis based method for dynamics and collective analysis of protein-protein interaction networks PY - 2017/11/21 AU - Shen, Yi-Zhen AU - Ding, Yong-Sheng AU - Gu, Quan UR - https://bridges.monash.edu/articles/dataset/Spectrum_analysis_based_method_for_dynamics_and_collective_analysis_of_protein-protein_interaction_networks/5619502 DO - 10.4225/03/5a13725619374 L4 - https://ndownloader.figshare.com/files/9783256 KW - Bioinformatics -- Congresses KW - Computational biology -- Congresses KW - Computer vision in medicine -- Congresses KW - Computational biology -- Methods -- Congresses KW - Pattern recognition, automated -- Methods -- Congresses KW - Protein–protein interactions KW - Evidence networks KW - Visual analytics KW - Exploratory analysis KW - Spectrum analysis KW - 2008 KW - conference paper KW - 1959.1/63706 KW - monash:7860 KW - Bioinformatics Software KW - Bioinformatics KW - Pattern Recognition and Data Mining N2 - The importance of understanding biological interaction networks has fueled the development of numerous interaction data generation techniques, databases and prediction tools. Generation of high-confident interaction networks formulates the first step towards the study for protein–protein interactions (PPI). A number of experimental methods, based on distinct, physical principles have been developed to identify PPI such as the yeast two-hybrid method (Y2H). In this work, we focus on one example of biological networks, namely the yeast protein interaction network (YPIN). In YPIN, we design and implement a computational model that captures the discrete and stochastic nature of protein interactions. In this model, we apply spectrum analysis method to the variance of the protein nodes which play an important role in the PPI networks, which can show the topology structure of dynamic and collective performances of PPI networks. We take YPIN, such as 48 "quasi-cliques" and 6 "quasi-bipartites" separated from 11855 yeast PPI networks with 2617 proteins, as an example and apply spectrum analysis to show the topology structure of dynamic and collective analysis of PPI networks and the performances. The obtained results may be valuable for deciphering unknown protein functions, determining protein complexes, and inventing drugs. PRIB 2008 proceedings found at: http://dx.doi.org/10.1007/978-3-540-88436-1 Contributors: Monash University. Faculty of Information Technology. Gippsland School of Information Technology ; Chetty, Madhu ; Ahmad, Shandar ; Ngom, Alioune ; Teng, Shyh Wei ; Third IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB) (3rd : 2008 : Melbourne, Australia) ; Coverage: Rights: Copyright by Third IAPR International Conference on Pattern Recognition in Bioinformatics. All rights reserved. ER -