Neural feature association rule mining for protein interaction prediction

Authors:
Jae-Hong Eom
Affiliations:
Biointelligence Laboratory, School of Computer Science and Engineering, Seoul National University, Seoul, South Korea
Venue:
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Year:
2006

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Abstract

The prediction of protein interactions is an important problem in post–genomic biology. In this paper, we present an association rule mining method for protein interaction prediction. A neural network is used to cluster protein interaction data and a feature selection is used to reduce the dimension of protein features. For model training, the preliminary network model was constructed with existing protein interaction data in terms of their functional categories and interactions. A set of association rules for protein interaction prediction are derived by decoding a set of learned weights of trained neural network after this model training. The protein interaction data of Yeast from public databases are used. The prediction performance was compared with simple association rule-based approach. According to the experimental results, proposed method achieved about 95.5% accuracy.