Abstract
Most of current methods mainly focus on topological information and fail to consider the information from protein primary sequence which is of considerable importance for protein complex detection. Based on this observation, we propose a novel algorithm called CDIP (Complex Detection based on Integrated Properties) to discover protein complexes from the yeast PPI network. In our method, a simple feature representation from protein primary sequence is presented and become a novel part of feature properties. The algorithm can consider both topological and biological information (amino acid background frequency), which is helpful to detect protein complex more efficiently. The experiments conducted on two public datasets show that the proposed algorithm outperforms the two state-of-the-art protein complex detection algorithms.
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Yu, Y., Lin, L., Sun, C., Wang, X., Wang, X. (2011). Complex Detection Based on Integrated Properties. In: Lu, BL., Zhang, L., Kwok, J. (eds) Neural Information Processing. ICONIP 2011. Lecture Notes in Computer Science, vol 7062. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24955-6_15
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DOI: https://doi.org/10.1007/978-3-642-24955-6_15
Publisher Name: Springer, Berlin, Heidelberg
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