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Issue No. 01 - Jan.-Feb. (2013 vol. 10)
ISSN: 1545-5963
pp: 219-225
Yuan Zhu , Dept. of Math., Guangdong Univ. of Bus. Studies, Guangzhou, China
Xiao-Fei Zhang , Dept. of Math., Sun Yat-Sen Univ., Guangzhou, China
Dao-Qing Dai , Dept. of Math., Sun Yat-Sen Univ., Guangzhou, China
Meng-Yun Wu , Dept. of Math., Sun Yat-Sen Univ., Guangzhou, China
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.
Proteins, Reliability, Biological system modeling, Humans, Data models, Noise measurement,PPI data denoising, Protein-protein interaction network, generative network model
Yuan Zhu, Xiao-Fei Zhang, Dao-Qing Dai, Meng-Yun Wu, "Identifying Spurious Interactions and Predicting Missing Interactions in the Protein-Protein Interaction Networks via a Generative Network Model", IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 10, no. , pp. 219-225, Jan.-Feb. 2013, doi:10.1109/TCBB.2012.164
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