title: Identifying spurious interactions and predicting missing interactions in the protein-protein interaction networks via a generative network model.
abstract: With the rapid development of high-throughput experiment techniques for protein-protein interaction (PPI) detection, a large amount of PPI network data are becoming available. However, the data produced by these techniques have high levels of spurious and missing interactions. This study assigns a new reliably indication for each protein pairs via the new generative network model (RIGNM) where the scale-free property of the PPI network is considered to reliably identify both spurious and missing interactions in the observed high-throughput PPI network. The experimental results show that the RIGNM is more effective and interpretable than the compared methods, which demonstrate that this approach has the potential to better describe the PPI networks and drive new discoveries.
authors.affiliation: Department of Mathematics, Guangdong University of Business Studies, Guangzhou, China. zhuyuan7@mail2.sysu.edu.cn
authors.forename: Yuan
authors.initials: Y
authors.lastname: Zhu
authors.forename: Xiao-Fei
authors.initials: XF
authors.lastname: Zhang
authors.forename: Dao-Qing
authors.initials: DQ
authors.lastname: Dai
authors.forename: Meng-Yun
authors.initials: MY
authors.lastname: Wu
mesh_terms: D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D030541:Databases, Genetic; D005656:Fungal Proteins / Q000235:genetics / Q000378:metabolism; D020869:Gene Expression Profiling; D006801:Humans; D008954:Models, Biological; D020411:Oligonucleotide Array Sequence Analysis; D025941:Protein Interaction Mapping / Q000379:methods*; D011506:Proteins / Q000235:genetics* / Q000378:metabolism*
chemical_list: D005656:Fungal Proteins; D011506:Proteins