Recognition of structure classification of protein folding by NN and SVM hierarchical learning architecture

Abstract

Classifying the structure of protein is a very important task in biological data. By means of the classification, the relationships and characteristics among known proteins can be exploited to predict the structure of new proteins. The study of the protein structures is based on the sequences and their similarity. It is a difficult task. Recently, due to the ability of machine learning techniques, many researchers have applied them to probe into this protein classification problem. We also apply here machine learning methods for multi-class protein fold recognition problem by proposing a novel hierarchical learning architecture. This novel hierarchical learning architecture can be formed by NN (neural networks) or SVM (support vector machine) as basic building blocks. Our results show that both of them can perform well. We use this new architecture to attack the multi-class protein fold recognition problem as proposed by Dubchak and Ding in 2001. With the same set of features our method can not only obtain better prediction accuracy and lower computation time, but also can avoid the use of the stochastic voting process in the original approach.