Automatic classification of protein structures based on convex hull representation by integrated neural network

  • Authors:

  • Yong Wang;Ling-Yun Wu;Xiang-Sun Zhang;Luonan Chen

  • Affiliations:

  • Osaka Sangyo University, Daito, Osaka, Japan;Academy of Mathematics and Systems Science, CAS, Beijing, China;Academy of Mathematics and Systems Science, CAS, Beijing, China;Osaka Sangyo University, Daito, Osaka, Japan

  • Venue:
  • TAMC'06 Proceedings of the Third international conference on Theory and Applications of Models of Computation
  • Year:
  • 2006

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Abstract

The large scale deposited data and existing manual classification scheme make it possible to study the automatic classification of protein structures in machine learning framework. In this paper the classification system is constructed by an integrated feedforward neural network through incorporating the expert judgements and existing classification schemes into the learning procedure. Since different aspects of a protein structure may be relevant to various biological problems, the protein structure is represented by the convex hull of its backbone and geometric features are extracted. The training and prediction tests for different training sets in the class level of CATH indicate that the new automatic classification scheme is effective and efficient. Also the neural network model outperforms hidden markov model and support vector machine in the comparison experiment.