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.
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Wang, Y., Wu, LY., Zhang, XS., Chen, L. (2006). Automatic Classification of Protein Structures Based on Convex Hull Representation by Integrated Neural Network. In: Cai, JY., Cooper, S.B., Li, A. (eds) Theory and Applications of Models of Computation. TAMC 2006. Lecture Notes in Computer Science, vol 3959. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11750321_48
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DOI: https://doi.org/10.1007/11750321_48
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34021-8
Online ISBN: 978-3-540-34022-5
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