Abstract
In this paper, we present an association rule based protein interaction prediction method. We use neural network to cluster protein interaction data and feature selection method to reduce protein feature dimension. After this model training, association rules for protein interaction prediction are generated by decoding a set of learned weights of trained neural network and association rule mining. For model training, the initial network model was constructed with existing protein interaction data in terms of their functional categories and interactions. The protein interaction data of Yeast (S.cerevisiae) from MIPS and SGD are used. The prediction performance was compared with traditional simple association rule mining method. According to the experimental results, proposed method shows about 96.1% accuracy compared to simple association mining approach which achieved about 91.4%.
This research was supported by the NRL Program of the Korea Ministry of Science and by the BK21-IT Program from the Ministry of Education and Human Resources Development of Korea. The ICT at Seoul National University provided research facilities for this study.
An erratum to this chapter can be found at http://dx.doi.org/10.1007/11550907_163 .
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Eom, JH., Zhang, BT. (2005). Prediction of Yeast Protein–Protein Interactions by Neural Feature Association Rule. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds) Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005. ICANN 2005. Lecture Notes in Computer Science, vol 3697. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550907_77
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