1. A Least Square Method Based Model for Identifying Protein Complexes in Protein-Protein Interaction Network
- Author
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Qiguo Dai, Yang Liu, Zhixia Teng, Xiaoyan Liu, Maozu Guo, and Yingjie Guo
- Subjects
Cytoplasm ,Article Subject ,General Immunology and Microbiology ,Basis (linear algebra) ,Homogeneity (statistics) ,lcsh:R ,Computational Biology ,Proteins ,lcsh:Medicine ,General Medicine ,Biology ,Models, Theoretical ,Bioinformatics ,General Biochemistry, Genetics and Molecular Biology ,Protein protein interaction network ,Ppi network ,Multiprotein Complexes ,Protein Interaction Maps ,Least-Squares Analysis ,Biological system ,Protein Interaction Map ,Algorithms ,Research Article - Abstract
Protein complex formed by a group of physical interacting proteins plays a crucial role in cell activities. Great effort has been made to computationally identify protein complexes from protein-protein interaction (PPI) network. However, the accuracy of the prediction is still far from being satisfactory, because the topological structures of protein complexes in the PPI network are too complicated. This paper proposes a novel optimization framework to detect complexes from PPI network, named PLSMC. The method is on the basis of the fact that if two proteins are in a common complex, they are likely to be interacting. PLSMC employs this relation to determine complexes by a penalized least squares method. PLSMC is applied to several public yeast PPI networks, and compared with several state-of-the-art methods. The results indicate that PLSMC outperforms other methods. In particular, complexes predicted by PLSMC can match known complexes with a higher accuracy than other methods. Furthermore, the predicted complexes have high functional homogeneity.
- Published
- 2014