Abstract
Protein–protein interactions (PPIs) play crucial roles in a number of biological processes. Recently, protein interaction networks (PINs) for several model organisms and humans have been generated, but few large-scale researches for mice have ever been made neither experimentally nor computationally. In the work, we undertook an effort to map a mouse PIN, in which protein interactions are hidden in enormous amount of biomedical literatures. Following a co-occurrence-based text-mining approach, a probabilistic model—naïve Bayesian was used to filter false-positive interactions by integrating heterogeneous kinds of evidence from genomic and proteomic datasets. A support vector machine algorithm was further used to choose protein pairs with physical interactions. By comparing with the currently available PPI datasets from several model organisms and humans, it showed that the derived mouse PINs have similar topological properties at the global level, but a high local divergence. The mouse protein interaction dataset is stored in the Mouse protein–protein interaction DataBase (MppDB) that is useful source of information for system-level understanding of gene function and biological processes in mammals. Access to the MppDB database is public available at http://bio.scu.edu.cn/mppi.
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Acknowledgments
We are grateful to Dr. Han Hu for his assistance on the website construction. We also thank Prof. Yongsheng Liu, Dr. Bo Liu and Dr. Guan Song for reading the manuscript and their useful suggestions. This work was supported partially by Doctoral Fund of Ministry of Education of China (Grant No. 200806101013) and Important National Science & Technology Specific Projects of China (Grant No. 2009ZX10005-020).
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X. Li and H. Cai contributed equally to this work.
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Li, X., Cai, H., Xu, J. et al. A mouse protein interactome through combined literature mining with multiple sources of interaction evidence. Amino Acids 38, 1237–1252 (2010). https://doi.org/10.1007/s00726-009-0335-7
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DOI: https://doi.org/10.1007/s00726-009-0335-7