Application of fuzzy subtractive clustering for enzymes classification

Authors:
Gita Sastria;Choong-Yeun Liong;Ishak Hashim
Affiliations:
Bioinformatics Research Centre, Institute of System Biology, Universiti Kebangsaan Malaysia, Bangi, Selangor, Darul Ehsan, Malaysia;School of Mathematical Sciences, Faculty of Science & Technology, Universiti Kebangsaan Malaysia, Bangi, Selangor, Darul Ehsan, Malaysia;Bioinformatics Research Centre, Institute of System Biology, Universiti Kebangsaan Malaysia, Bangi, Selangor, Darul Ehsan, Malaysia
Venue:
ACC'08 Proceedings of the WSEAS International Conference on Applied Computing Conference
Year:
2008

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Abstract

Enzymes are a subclass of proteins that are specialized in catalytic activity. Protein classification problem is a difficult task because of the complexity in function and structural characteristics. This brings the necessity of computer-based methods like machine learning, artificial intelligence and data mining to solving the protein classification problems. The goal of this study is to propose the application of Fuzzy Subtractive Clustering (FSC) technique to classify the function of an enzyme by analyzing its structural class similarity to families of enzymes. A codification scheme was implemented to convert the primary structure of enzymes into a real-valued vector. To evaluate our study, the dataset obtained from Protein Data Bank (PDB) family database are used as the experimental datasets. The computational results have shown that FSC technique gives a better overall success prediction rate of 84.53% on average in comparison to previously published results.