Prediction of yeast protein-protein interactions by neural feature association rule

Authors:
Jae-Hong Eom;Byoung-Tak Zhang
Affiliations:
Biointelligence Lab., School of Computer Science and Engineering, Seoul National University, Seoul, South Korea;Biointelligence Lab., School of Computer Science and Engineering, Seoul National University, Seoul, South Korea
Venue:
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
Year:
2005

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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%.